Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

Dubovyk, Olena; Landmann, Tobias; Erasmus, Barend; Tewes, Andreas; Schellberg, J.

doi:10.1016/j.jag.2015.01.002

Cited by 54 publications

(32 citation statements)

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“…quired over a single region have allowed for Landsat based time series analysis approaches. Since vegetation indices (or more generally spectral indices) from satellite imagery have been commonly used to monitor, analyze and map temporal and spatial dynamics of post-fire environments (Chu and Guo, 2014), spectral index time series analysis has been a multitemporal procedure particularly useful to monitor post-fire vegetation (Bastos et al, 2011;Chen et al, 2014;Dubovyk et al, 2015;Lanorte et al, 2014;Riaño et al, 2002;van Leeuwen et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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Mediterranean ecosystems are adapted to recurrent forest fires by having regeneration mechanisms that overcome the immediate effects of fire. However, the increasing frequency of fires in most European Mediterranean countries is challenging the natural regrowth capability of these ecosystems. In this context, monitoring post-fire vegetation recovery is a priority for forest management and soil erosion control. In this work, a 13-year series (1999-2011) of Landsat 5 Thematic Mapper (TM)/Landsat 7 Enhanced Thematic Mapper (ETM +) data was used to model post-fire vegetation recovery as a function of burn severity and to quantify post-fire resilience as a measure of vegetation cover regrowth. We evaluated a large forest fire located in Spain that burned approximately 30 km 2 of Pinus pinaster Ait. in August 1998. 88 field plots of four burn severity levels (unburned, low, moderate and high) were measured in the field a year after the fire. As a variable representative of vegetation, we chose the shade normalized green vegetation fraction image (SGV) obtained by applying Multiple Endmember Spectral Mixture Analysis (MESMA) to the original Landsat TM/ETM + images. The SGV values were extracted for the 88 field plots and, after performing a one-way analysis of variance (ANOVA), a Fisher's Least Significant Difference (LSD) test allowed us to estimate resilience of vegetation cover as the number of post-fire years exhibiting a statistically significant difference between burned and unburned areas. Next, SGV values were referenced to unburned control plots values and the vegetation recovery index (VRI) was defined. The evolution in time curve of VRI for low, moderate and highly fire affected vegetation was fit using trend models (specifically, an exponential trend for VRI in high and moderate burn severity levels; a linear trend for low burn severity level, Root Mean Square Error, RMSE = 0.18, 0.13, and 0.09, respectively). We observed that vegetation cover affected by low severity fire recovered to its original state after 7 years, and vegetation cover affected by moderate severity recovered after 13 years. Vegetation affected by high severity fire was estimated to recover after 20 years. We conclude that VRI time series based on multitemporal MESMA fractions from Landsat data can be considered a valuable indicator of the post-fire vegetation cover recovery. Its temporal evolution represented post-fire vegetation cover regrowth adequately and facilitated the estimate of vegetation cover resilience in Mediterranean forests.

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Section: Introductionmentioning

confidence: 99%

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Fernández–Manso

Quintano

Roberts

2016

Remote Sensing of Environment

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“…Mann-Kendall's test and Theil-Sen median slope were used to obtain the possible monotonic trend in the time series of the annual average EVI at the pixel level, which were widely used to determine vegetation variation [57][58][59]. The Mann-Kendall's test [60] is a robust nonparametric significance test that calculates Z values based on the signs of changes in the time series.…”

Section: Pixel-based Non-parametric Trend Analyses Of Evimentioning

confidence: 99%

The Greenness of Major Shrublands in China Increased from 2001 to 2013

et al. 2016

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Abstract:Shrubs have been reported to expand into grassland and polar regions in the world, which causes complex changes in ecosystem carbon, nutrients, and resilience. Given the projected global drying trend, shrubs with their superior drought resistance and tolerance may play more important roles in global ecosystem function. Shrubland exists in all of the climate zones in China, from subtropical to temperate and high cold regions, and they occupy more than 20% of the land area. In this paper, we analyzed the spatiotemporal trend of MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) for six shrubland types in China from 2001 to 2013 and its relationship to intra-and inter-annual regional climate dynamics. Existing literature reported that the vegetation index did not change significantly in China during 2000-2012. However, we found that the shrubland EVI in China increased significantly at a rate of 1.01ˆ10´3 EVI¨a´1 from 2001 to 2013. Two major shrubland types (subtropical evergreen and temperate deciduous) and two desert types (high-cold desert and temperate desert) increased significantly, whereas subalpine evergreen shrubland decreased at a rate of´0.64ˆ10´3 EVI¨a´1. We also detected a significantly lengthened growing season of temperate deciduous shrubland. The growing season length contributed significantly to the annual averaged EVI for temperate deciduous, subalpine deciduous and subtropical evergreen shrublands. Furthermore, the precipitation variation contributed more to the annual averaged EVI than the temperature. The year-round decrease in rainfall and the increase in temperature led to a significant reduction in the subalpine evergreen shrubland EVI. The enhancement of countrywide shrubland EVI may promote its contribution to the regional ecosystem function and its potential to invade grasslands.

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“…Due to natural and/or anthropogenic causes, vegetation is endlessly changing at a variety of spatial and temporal scales [2,[4][5][6]. Changes in vegetation play crucial roles in regulating the carbon balance, reducing greenhouse gases and maintaining climate stability across various spatial and temporal scales [5,7].…”

Section: Introductionmentioning

confidence: 99%

Detecting the Temporal Scaling Behavior of the Normalized Difference Vegetation Index Time Series in China Using a Detrended Fluctuation Analysis

et al. 2015

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Abstract:Vegetation is an important part of terrestrial ecosystems. Although vegetation dynamics have explicit spatial and temporal dimensions, the study of the temporal process is in its infancy. Evaluation of temporal scaling behavior could provide a unique perspective for exploring the temporal nature of vegetation dynamics. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to reflect vegetation dynamics, and the temporal scaling behavior of the NDVI in China was determined via detrended fluctuation analysis (DFA). Our main objectives were to reveal the temporal scaling behavior of NDVI time series and to understand variation among vegetation types. First, DFA revealed similar exponents, which ranged from 0.6 to 0.9, for all selected pixels, implying that a long-range correlation was generally present in the NDVI time series at the individual pixel scale. We then extended the analysis to all of China and found that 99.30% of the pixel exponents ranged from 0.5 to 1. These results suggest that the NDVI time series displays strong long-range correlation throughout most of China; however, the exponents exhibited regional variability. To explain these differences, OPEN ACCESSRemote Sens. 2015, 7 12943we further analyzed the exponents for 12 vegetation types based on a vegetation map of China. All of the vegetation types exhibited well-defined long-range correlation, with exponents ranging from 0.7189 to 0.8436. For all vegetation types, the maximum and average value and standard deviation of the exponents decreased with increasing annual maximum NDVI values, suggesting that low vegetation density is much more sensitive to external factors. These findings may be useful for understanding vegetation dynamics as a complex, temporally varying phenomenon.

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Monitoring vegetation dynamics with medium resolution MODIS-EVI time series at sub-regional scale in southern Africa

Cited by 54 publications

References 50 publications

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

Burn severity influence on post-fire vegetation cover resilience from Landsat MESMA fraction images time series in Mediterranean forest ecosystems

The Greenness of Major Shrublands in China Increased from 2001 to 2013

Detecting the Temporal Scaling Behavior of the Normalized Difference Vegetation Index Time Series in China Using a Detrended Fluctuation Analysis

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