Association of vegetation indices with atmospheric &amp; biological factors using MODIS time series products

Bari, Ehsanul; Nipa, Nusrat Jahan; Roy, Bishal

doi:10.1016/j.envc.2021.100376

Cited by 20 publications

(10 citation statements)

References 53 publications

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“…However, the response of urban vegetation land cover to multiple climatic stressors varies interannually and in different seasons of the years, related to different vegetation type's characteristics and climatic conditions. The urban ecosystem functions regulate the relationships between urban green land cover and climate parameters 8 . Climate warming and the increase frequency of extreme climate events (key manifestations of complex systems, in both the natural and human world, which occurred during the last years in Europe are of the great environmental concerns.…”

Section: Introductionmentioning

confidence: 99%

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Zoran,

Savastru,

Savastru

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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Urban vegetation and its carbon storage capacity are critical factors for terrestrial carbon cycling and global sustainable development goals (SDGs). Scientific research for a better managing of urban green land cover is an essential issue of the sustainable urban development initiative promoted by the EU governments. Owing to a crucial role that urban vegetation perform in an urban ecosystem, being effective in mitigating the air pollution and urban heat island effect by canopy shade and evapotranspiration, studying the vegetation dynamics in terms of its derived satellite biogeophysical variables becomes inevitable in the context of climate-smart city planning and design. However, in order to explain the urban-rural gradient in vegetation greenness trends and its responses to climate and antropogenic drivers of land-cover changes this study used time-series of derived satellite data (normalized difference vegetation index-NDVI, enhanced vegetation index-EVI, land surface temperature-LST, leaf area index LAI, and fraction of photosynthetically active radiation absorbed by vegetation-FPAR). Satellite datasets of MODIS Terra, and Landsat TM/ETM + have been used for urban vegetation analysis over 2002-2022 period of Bucharest metropolitan area in Romania. Was found that the average decrease in vegetation land cover was 0.1 to 0.3. The correlation analyses revealed that, at the pixel-scale, LST possessed a strong positive correlation with NDVI, EVI, FPAR and LAI during the entire investigated period. Only during summer period NDNI/EVI are inversely correlated with LST (r= -0.67; p<0.01) The spatio-temporal pattern of urban/periurban vegetation dynamics trends and their association with other atmospheric, biological, and soil indicators need to be studied with different satellite sensors and resolutions over the long-term periods of time.

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Section: Introductionmentioning

confidence: 99%

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Zoran,

Savastru,

Savastru

et al. 2023

Ninth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2023)

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“…Satellite imagery and data obtained by Remote Sensing (RS) and Geographic Information Systems (GIS) have enhanced our ability to notice intricate changes on the Earth's surface [ 15 ]. With this revolution, it has been considerably easier to identify changes across a large geographical area and over time [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

“…With this revolution, it has been considerably easier to identify changes across a large geographical area and over time [ 16 ]. The thermal conditions of cities and their surroundings are strongly influenced by human-oriented development and related land-use conversion of the natural landscape into impermeable surfaces [ 15 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Using remotely sensed satellite imagery, a change in surface temperature may be observed, which is an indicator of impervious surface build-up [ 26 ]. The distribution of LST is greatly influenced by the presence of natural vegetation [ 15 , 27 , 28 , 29 ]. To study variations in LST, the Normalized Difference Vegetation Index (NDVI) is often used [ 12 , 30 , 31 ].…”

Section: Introductionmentioning

confidence: 99%

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Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine

Roy

Bari²

2022

Heliyon

Self Cite

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“…However, since a single remote sensing index is greatly influenced by atmospheric and soil factors, its assessment results have certain limitations. For example, the Normalized Difference Vegetation Index (NDVI) is related to humidity, and the Enhanced Vegetation Index (EVI) is affected by soil moisture [ 14 ]. It is more reasonable to combine a single remote sensing index with various other remote sensing data, such as precipitation and soil data, to monitor desertification [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV

Xue

Liu

Han

et al. 2022

IJERPH

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Grassland desertification has become one of the most serious environmental problems in the world. Grasslands are the focus of desertification research because of their ecological vulnerability. Their application on different grassland desertification grades remains limited. Therefore, in this study, 19 vegetation indices were calculated for 30 unmanned aerial vehicle (UAV) visible light images at five grades of grassland desertification in the Mu Us Sandy. Fractional Vegetation Coverage (FVC) with high accuracy was obtained through Support Vector Machine (SVM) classification, and the results were used as the reference values. Based on the FVC, the grassland desertification grades were divided into five grades: severe (FVC < 5%), high (FVC: 5–20%), moderate (FVC: 21–50%), slight (FVC: 51–70%), and non-desertification (FVC: 71–100%). The accuracy of the vegetation indices was assessed by the overall accuracy (OA), the kappa coefficient (k), and the relative error (RE). Our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. Excess Green Red Blue Difference Index (EGRBDI), Visible Band Modified Soil Adjusted Vegetation Index (V-MSAVI), Green Leaf Index (GLI), Color Index of Vegetation Vegetative (CIVE), Red Green Blue Vegetation Index (RGBVI), and Excess Green (EXG) accurately assessed grassland desertification at severe, high, moderate, and slight grades. In addition, the Red Green Ratio Index (RGRI) and Combined 2 (COM2) were accurate in assessing severe desertification. The assessment of the 19 indices of the non-desertification grade had low accuracy. Moreover, our result showed that the accuracy of SVM-supervised classification was high in assessing each grassland desertification grade. This study emphasizes that the applicability of the vegetation indices varies with the degree of grassland desertification and hopes to provide scientific guidance for a more accurate grassland desertification assessment.

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Association of vegetation indices with atmospheric & biological factors using MODIS time series products

Cited by 20 publications

References 53 publications

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Exploring with time-series satellite data of multiple stressors effects on urban/periurban vegetation

Examining the relationship between land surface temperature and landscape features using spectral indices with Google Earth Engine

Accuracy of Vegetation Indices in Assessing Different Grades of Grassland Desertification from UAV

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