Estimation of interannual variation in productivity of global vegetation using NDVI data

Chen, Z. M.; Babiker, Insaf S.; Chen, Z. X.; Komaki, Kentaro; Mohamed, Musbah; Kato, Kikou

doi:10.1080/0143116032000160435

Cited by 31 publications

(12 citation statements)

References 47 publications

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“…Quantitative studies of the relationship between LULC patterns and LST are important for land-use management and planning. Furthermore while NDVI has been used for the estimation of vegetation productivity and rainfall in semi-arid areas (Chen et al, 2004;Wang et al, 2004), the Normalized Difference Built-up Index (NDBI) has been developed for the identification of urban and built-up areas (Zha et al, 2003). It is therefore possible that the utilization of both NDVI and NDBI as surrogates of LULC can reveal the relationships between different indices such as NDVI, NDBI, and land surface temperature in UHI studies.…”

Section: Introductionmentioning

confidence: 99%

Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

Zhang

Odeh

Han

2009

International Journal of Applied Earth Observation and Geoinfor

294

150

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Section: Introductionmentioning

confidence: 99%

Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

Zhang

Odeh

Han

2009

International Journal of Applied Earth Observation and Geoinfor

294

150

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“…In arid or semi-arid region, normalized difference vegetation index (NDVI) is often used for the estimation of green vegetation cover (Pickup et al 1993, Chen et al 2006) and productivity (Chen et al 2004, Wang et al 2004. NDVI is based on the spectral properties generated by photosynthetic process: it compares the ratio between visible red light, which is strongly absorbed, and near-infrared, which is strongly reflected by green vegetation.…”

Section: Satellite Imagery and Ndvi Estimatesmentioning

confidence: 99%

Comparison of field- and satellite-based vegetation cover estimation methods

Kim

Narantsetseg

et al. 2017

j ecology environ

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Background: Monitoring terrestrial vegetation cover condition is important to evaluate its current condition and to identify potential vulnerabilities. Due to simplicity and low cost, point intercept method has been widely used in evaluating grassland surface and quantifying cover conditions. Field-based digital photography method is gaining popularity for the purpose of cover estimate, as it can reduce field time and enable additional analysis in the future. However, the caveats and uncertainty among field-based vegetation cover estimation methods is not well known, especially across a wide range of cover conditions. We compared cover estimates from point intercept and digital photography methods with varying sampling intensities (25, 49, and 100 points within an image), across 61 transects in typical steppe, forest steppe, and desert steppe in central Mongolia. We classified three photosynthetic groups of cover important to grassland ecosystem functioning: photosynthetic vegetation, non-photosynthetic vegetation, and bare soil. We also acquired normalized difference vegetation index from satellite image comparison with the field-based cover. Results: Photosynthetic vegetation estimates by point intercept method were correlated with normalized difference vegetation index, with improvement when non-photosynthetic vegetation was combined. For digital photography method, photosynthetic and non-photosynthetic vegetation estimates showed no correlation with normalized difference vegetation index, but combining of both showed moderate and significant correlation, which slightly increased with greater sampling intensity. Conclusions: Results imply that varying greenness is playing an important role in classification accuracy confusion. We suggest adopting measures to reduce observer bias and better distinguishing greenness levels in combination with multispectral indices to improve estimates on dry matter.

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“…According to the Intergovernmental Panel on Climate Change (IPCC, 2001), the Earth's climate has warmed by 0.6°C over the past 100 years. Certainly, global warming controls vegetation dynamic and its inter-annual change (Chen et al, 2004). Thus, increases in spring temperatures following by an earlier start of vegetation growing season are reported to be the main driving force for increase in vegetation growth in the northern high latitudes of Eurasia and North America .…”

Section: Introductionmentioning

confidence: 99%

Inter-Annual Changes in Vegetation Activities and Their Relationship to Temperature and Precipitation in Central Asia from 1982 to 2003

Propastin

Kappas²,

Muratova³

2008

J ENV INFORM

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ABSTRACT. We analyzed inter-annual trends in annual and seasonal vegetation activities in Central Asia from 1982 to 2003 and their correlation to climate variability using the NOAA/AVHRR Normalized Difference Vegetation Index (NDVI) dataset and a gridded climate dataset. The results indicate a significant increase in NDVI with a value of 11.35% over the growing season during the 22-year period. Totalled over the entire vegetated area, about 35% of all pixels exhibited significant upward trend in growing season NDVI. We found that NDVI increase in spring was the main contributor to the general upward trend, the spring NDVI increased in more than 50% of all pixels and showed an average value of 13.58%. Correlation analysis indicated a gradual rise in temperature as the only factor controlling trend in spring NDVI. Significant increase in vegetation activity was also identified for summer season, but its amplitude (9.23%) and comprising area (25.13% of all vegetated pixels) were less than for spring. Downward trends in growing season NDVI occurred in 2.17% of the total vegetated area. The greening trends of spring, growing season and summer NDVI strongly related with the climatic parameters: for each land cover type, we found significant correlation with spring temperature and total precipitation; 75% of all upward trends in growing season NDVI were explained by the combination of these both variables. We found that the NDVI trends and their climatic correlates demonstrate great spatial variability at the scale of individual land cover types and at per-pixel scale and proofed that the land use change caused by the constitutional change in the 1991 has substantial control on the vegetation trends. Increased vegetation growth indicated through the analysis of NOAA AVHRR NDVI time-series suggests an increasing carbon stock in biomass of ecosystems in Central Asia.

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Estimation of interannual variation in productivity of global vegetation using NDVI data

Cited by 31 publications

References 47 publications

Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis

Comparison of field- and satellite-based vegetation cover estimation methods

Inter-Annual Changes in Vegetation Activities and Their Relationship to Temperature and Precipitation in Central Asia from 1982 to 2003

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