Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico

Neeti, Neeti; Rogan, John; Christman, Zachary; Eastman, J. Ronald; Millones, Marco; Schneider, Laura; Nickl, Elsa; Schmook, Birgit; Turner, B. L.; Ghimire, Bardan

doi:10.1080/01431161.2011.616238

Cited by 49 publications

(36 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…R 2 values can approach 0 under a correct model if the residual variance of y is always close to the total variance of y [35]. Numerous studies [36][37][38] identify harmonic regression as an appropriate model for our task, i.e., extraction of seasonal curve parameters and data smoothing. Thus, no additional verification of the model fit was required.…”

Section: Calculating Seasonality Metrics From Ndvi and Climatic Datamentioning

confidence: 99%

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

et al. 2016

View full text Add to dashboard Cite

Abstract:Currently there is a lack of quantitative information regarding the driving factors of vegetation dynamics in post-Soviet Central Asia. Insufficient knowledge also exists concerning vegetation variability across sub-humid to arid climatic gradients as well as vegetation response to different land uses, from natural rangelands to intensively irrigated croplands. In this study, we analyzed the environmental drivers of vegetation dynamics in five Central Asian countries by coupling key vegetation parameter "overall greenness" derived from Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI time series data, with its possible factors across various management and climatic gradients. We developed nine generalized least-squares random effect (GLS-RE) models to analyze the relative impact of environmental factors on vegetation dynamics. The obtained results quantitatively indicated the extensive control of climatic factors on managed and unmanaged vegetation cover across Central Asia. The most diverse vegetation dynamics response to climatic variables was observed for "intensively managed irrigated croplands". Almost no differences in response to these variables were detected for managed non-irrigated vegetation and unmanaged (natural) vegetation across all countries. Natural vegetation and rainfed non-irrigated crop dynamics were principally associated with temperature and precipitation parameters. Variables related to temperature had the greatest relative effect on irrigated croplands and on vegetation cover within the mountainous zone. Further research should focus on incorporating the socio-economic factors discussed here in a similar analysis.

show abstract

Section: Calculating Seasonality Metrics From Ndvi and Climatic Datamentioning

confidence: 99%

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

et al. 2016

View full text Add to dashboard Cite

show abstract

“…The TS trend estimator is insensitive to outliers and it has a breakdown bound of 29% [42,43,49,50]. This simply implies that the values of the TS slope tolerate outliers up to 29% of the observations used in the time series without degradation of its accuracy [44,51]. STA was also applied to the rainfall (TAMSAT) data for better comparison with NDVI.…”

Section: Analysis Of Seasonal Trendmentioning

confidence: 99%

“…To perform CMK test, three stepwise approach are involved. The first step account for the elimination of serial correlation in the NDVI and rainfall time series which might have influence on the trend test using a method known as prewhitening [44,53]. Prewhitening removes serial correlation from the residuals of both NDVI and rainfall while maintaining the trends in the dataset [55,56].…”

Section: Significance Test Of Sta Using Contextual Mann-kendall (Cmk)mentioning

confidence: 99%

“…Trends in vegetation dynamics for each pixels (NDVI time series) was statistically examined in order to identify areas undergoing similar trends [44]. In order to evaluate the significance of trends during the Theil-Sen slope calculation (see section 2.3.1), a non-parametric statistic test known as Contextual Mann-Kendall (CMK) was applied [52].…”

Section: Significance Test Of Sta Using Contextual Mann-kendall (Cmk)mentioning

confidence: 99%

“…In the first stage of analysis, each annual imagery in the time series (1983-2011) are subjected to harmonic regression in order to approximate the seasonal curve thereby producing five greening parameters, amplitude 0 (annual mean image), amplitude 1 and phase 1 (annual cycle), amplitude 2 and phase 2 (the semi-annual cycle) [42,44,45]. The overall greenness (mean annual NDVI for each year) is described as Amplitude 0, the magnitude of peak of annual greenness as amplitude 1, while the timing of annual peak of greenness is referred to as phase 1 [44]. An increase or decrease in phase 1 indicates shift in the timing of greening event to an earlier or later time of the year respectively.…”

Section: Analysis Of Seasonal Trendmentioning

confidence: 99%

See 2 more Smart Citations

Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets

Osunmadewa¹,

Zewdie²,

Csaplovics³

et al. 2018

Open Geosciences

View full text Add to dashboard Cite

Abstract:Time series data are of great importance for monitoring vegetation phenology in the dry sub-humid regions where change in land cover has influence on biomass productivity. However few studies have inquired into examining the impact of rainfall and land cover change on vegetation phenology. This study explores Seasonal Trend Analysis (STA) approach in order to investigate overall greenness, peak of annual greenness and timing of annual greenness in the seasonal NDVI cycle. Phenological pattern for the start of season (SOS) and end of season (EOS) was also examined across different land cover types in four selected locations. A significant increase in overall greenness (amplitude 0) and a significant decrease in other greenness trend maps (amplitude 1 and phase 1) was observed over the study period. Moreover significant positive trends in overall annual rainfall (amplitude 0) was found which follows similar pattern with vegetation trend. Variation in the timing of peak of greenness (phase 1) was seen in the four selected locations, this indicate a change in phenological trend. Additionally, strong relationship was revealed by the result of the pixel-wise regression between NDVI and rainfall. Change in vegetation phenology in the study area is attributed to climatic variability than anthropogenic activities.

show abstract

Analysis of remotely sensed and surface data of aerosols and meteorology for the Mexico Megalopolis Area between 2003 and 2015

et al. 2017

View full text Add to dashboard Cite

This paper presents an aerosol characterization study from 2003 to 2015 for the Mexico City Metropolitan Area using remotely sensed aerosol data, ground‐based measurements, air mass trajectory modeling, aerosol chemical composition modeling, and reanalysis data for the broader Megalopolis of Central Mexico region. The most extensive biomass burning emissions occur between March and May concurrent with the highest aerosol optical depth, ultraviolet aerosol index, and surface particulate matter (PM) mass concentration values. A notable enhancement in coarse PM levels is observed during vehicular rush hour periods on weekdays versus weekends owing to nonengine‐related emissions such as resuspended dust. Among wet deposition species measured, PM2.5, PM10, and PMcoarse (PM10‐PM2.5) were best correlated with NH4+, SO42−, and Ca2+, suggesting that the latter three constituents are important components of the aerosol seeding raindrops that eventually deposit to the surface in the study region. Reductions in surface PM mass concentrations were observed in 2014–2015 owing to reduced regional biomass burning as compared to 2003–2013.

show abstract

Mapping seasonal trends in vegetation using AVHRR-NDVI time series in the Yucatán Peninsula, Mexico

Cited by 49 publications

References 27 publications

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

Quantifying the Impacts of Environmental Factors on Vegetation Dynamics over Climatic and Management Gradients of Central Asia

Spatio-temporal monitoring of vegetation phenology in the dry sub-humid region of Nigeria using time series of AVHRR NDVI and TAMSAT datasets

Analysis of remotely sensed and surface data of aerosols and meteorology for the Mexico Megalopolis Area between 2003 and 2015

Contact Info

Product

Resources

About