MODIS EVI and LST Temporal Response for Discrimination of Tropical Land Covers

Phompila, Chittana; Lewis, Mervyn K.; Ostendorf, Bertram; Clarke, Kenneth

doi:10.3390/rs70506026

Cited by 25 publications

(15 citation statements)

References 31 publications

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“…5 have not yet been considered in this study. In addition, the enhanced vegetation index (EVI) was more sensitive than NDVI in some regions, such as tropical rainforests with high biomass [83]. The saturation of NDVI might underestimate the correlation between LST and vegetation cover.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Song

Qiu

et al. 2018

Remote Sensing

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Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM2.5 increased by 0.17 K, 0.04, and 1.02 μg/m3 in the period of 2001–2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72°N and 48°S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM2.5 on LST was more complex. On the whole, LST increased with a small increase in PM2.5 concentrations but decreased with a marked increase in PM2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.

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Section: Limitations and Future Researchmentioning

confidence: 99%

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Song

Qiu

et al. 2018

Remote Sensing

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“…where G is the gain factor, NIR is the Near Infra-red band, C1 and C2 are the coefficients of the aerosol resistance term, and L is the canopy background. Whilst the Normalized Difference Vegetation Index (NDVI) [49] is commonly used [22,45], as in a number of other forest studies, we chose the EVI, [50][51][52] due to its improved performance in areas of high biomass as a result of a de-coupling of canopy background signals and a reduction in atmospheric influences [53]. We used the MODIS EVI product MOD13Q1 at 250 m spatial and a 16-day temporal resolution downloaded from the United States Geological Survey (USGS; Table 1).…”

Section: Modis Evimentioning

confidence: 99%

Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

et al. 2018

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Afromontane forests are biodiversity hotspots and provide essential ecosystem services. However, they are under pressure as a result of an expanding human population and the impact of climate change. In many instances electric fencing has become a necessary management strategy to protect forest integrity and reduce human-wildlife conflict. The impact of confining hitherto migratory elephant populations within forests remains unknown, and monitoring largely inaccessible areas is challenging. We explore the application of remote sensing to monitor the impact of confinement, employing the Breaks For Additive Season and Trend (BFAST) time-series decomposition method over a 15-year period on Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) (MOD13Q1) datasets for two Kenyan forests. Results indicated that BFAST was able to identify disturbances from anthropogenic, fire and elephant damage. Sequential monitoring enabled the detection of gradual changes in the forest canopy, with degradation and regeneration being observed in both sites. Annual rates of forest loss in both areas were significantly lower than reported in other studies on Afromontane forests, suggesting that installing fences has reduced land-use conversion from human-related disturbances. Negative changes in EVI were predominantly gradual degradation rather than large-scale, abrupt clearings of the forest. Results presented here demonstrate that BFAST can be used to monitor biotic and abiotic drivers of change in Afromontane forests.

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“…For decades, the use of satellite-derived data for estimating LST has been well reported in the literature (Jimenez-Munoz, Sobrino 2003, Zhang et al 2006, Xiao et al 2007, David 2008, Nwilo et al 2012, Oguz 2013, Zaharaddeen et al 2016, Jeevalakshmi et al 2017, Deng et al 2018, Tarawally et al 2018. It has also been suggested that the inclusion of LST can improve land cover and vegetation monitoring (Mildrexler et al 2007, Sobrino, Julien 2013, Phompila et al 2015. Also, change in land cover is an important indicator that affects LST.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

Alademomi

Okolie

Daramola

et al. 2020

Quaestiones Geographicae

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The Lagos Lagoon is under increased pressure from growth in human population, growing demands for natural resources, human activities, and socioeconomic factors. The degree of these activities and the impacts are directly proportional to urban expansion and growth. In the light of this situation, the objectives of this study were: (i) to estimate through satellite imagery analysis the extent of changes in the Lagos Lagoon environment for the periods 1984, 2002, 2013 and 2019 using Landsat-derived data on land cover, Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI); and (ii) to evaluate the relationship between the derived data and determine their relative influence on the lagoon environment. The derived data were subjected to descriptive statistics, and relationships were explored using Pearson's correlation and regression analysis. The effect of land cover on LST was measured using the Contribution Index and a trend analysis was carried out. From the results, the mean LSTs for the four years were 22.68°C (1984), 24.34°C (2002), 26.46°C (2013) and 28.40°C (2019). Generally, the mean LSTs is in opposite trend with the mean NDVIs and EVIs as associated with their dominant land cover type. The strongest positive correlations were observed between NDVI and EVI while NDVI had the closest fit with LST in the regression. Built-up areas have the highest contributions to LST while vegetation had a cooling influence. The depletion in vegetative cover has compromised the biodiversity of this environment and efforts are required to reverse this trend.

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MODIS EVI and LST Temporal Response for Discrimination of Tropical Land Covers

Cited by 25 publications

References 31 publications

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Global Land Surface Temperature Influenced by Vegetation Cover and PM2.5 from 2001 to 2016

Detecting Vegetation Change in Response to Confining Elephants in Forests Using MODIS Time-Series and BFAST

Assessing the Relationship of LST, NDVI and EVI with Land Cover Changes in the Lagos Lagoon Environment

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