Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability

Zewdie, Worku; Csaplovics, Elmar; Inostroza, Luis

doi:10.1016/j.apgeog.2016.12.019

Cited by 93 publications

(50 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Climate change has significantly increased the vegetation cover in 54.1% of Southwest China (Liu, Zhang, Lin, & Xu, ). Climate change and human activities are the main factors that were considered in several previous studies (Li, Kuang, Huang, & Zhang, ; Piao et al, ; Zewdie, Csaplovics, & Inostroza, ). However, evapotranspiration, runoff, and soil moisture also affect vegetation dynamics (Liu, Adam, Richey, Zhu, & Myneni, ).…”

Section: Introductionmentioning

confidence: 99%

A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

Mengmeng

Xia

2018

Land Degrad Dev

View full text Add to dashboard Cite

Since 2000, the gross domestic product (GDP) of the Pearl River Delta (PRD) has increased by more than eight‐fold. Experiences throughout the world indicate that the impacts of economic development on vegetation are particularly important. This study analyzed the variations in vegetation coverage in the PRD from 2000 to 2016. Moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data were employed, whereas climate data, GDP, and population were also used as influencing factors. Topographic factors were introduced to explain the spatial vegetation patterns, and the impacts of human activities on vegetation were explored. The results revealed (a) the overall vegetation coverage in the PRD showed an increasing trend, and the overall average NDVI increased by 0.0033/a. (b) The spatial pattern did not change greatly, but the high and low NDVI values moved to low elevations and shallow topographic slopes where human activities were prevalent. This trend resulted in increased NDVI values in 38% of the area of the PRD (p < 0.01). (c) There was a positive correlation between NDVI and economic growth (GDP, population), with correlation coefficients greater than 0.8 (p < 0.01). NDVI is influenced by climate change and human activities (such as afforestation, resident lifestyles, and economic development policies). This study reveals that with good policies, both rapid economic development and vegetation coverage improvements can be simultaneously achieved in a region. The findings of this study are crucial for the sustainable development of the mega‐urban agglomerations of the PRD and Guangdong‐Hong Kong‐Macau Grand Bay.

show abstract

Section: Introductionmentioning

confidence: 99%

A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

Mengmeng

Xia

2018

Land Degrad Dev

View full text Add to dashboard Cite

show abstract

“…Vegetation parameters, such as the description of phenological patterns, can be estimated from time series of vegetation indexes derived from remote sensing, and among them, NDVI (Normalized Difference Vegetation Index) [20] has been widely used to monitor vegetation dynamics. There are studies that have examined, for example, the beginning and end periods of the vegetation growing season [21,22], the evaluation of selective cutting effects and climate change on vegetation [23][24][25], and the evaluation of post-fire effects and regeneration of vegetation [26][27][28]. Time series of indices such as NDVI are influenced by climate and surface changes, besides being vulnerable to noise such as atmospheric effects and different acquisition angles [25,29].…”

Section: Introductionmentioning

confidence: 99%

“…There are studies that have examined, for example, the beginning and end periods of the vegetation growing season [21,22], the evaluation of selective cutting effects and climate change on vegetation [23][24][25], and the evaluation of post-fire effects and regeneration of vegetation [26][27][28]. Time series of indices such as NDVI are influenced by climate and surface changes, besides being vulnerable to noise such as atmospheric effects and different acquisition angles [25,29]. Trend detection in time series relies on filtering the series for noise attenuation, as well as in the detection of changes from regression methods, temporal autocorrelation or non-parametric methods such as the Mann-Kendall test [30].…”

Section: Introductionmentioning

confidence: 99%

“…Most of the studies that evaluated the influence of disturbances on vegetation temporal dynamics are concentrated in areas of high and mid latitude [28,[31][32][33][34][35], and the few studies carried out on savannas biomes are located mainly in Africa and Australia [25,[36][37][38]; furthermore, publications related to the effects of fire in Brazilian savanna are geographically and taxonomically biased [39].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

Santana

2018

Fire

View full text Add to dashboard Cite

Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)-MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS-NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS-NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS-NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.

show abstract

“…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]. The trend in the prewhitened series is similar to the original data while having no serial correlation [53].…”

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

confidence: 99%

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

Self Cite

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

Monitoring ecosystem dynamics in northwestern Ethiopia using NDVI and climate variables to assess long term trends in dryland vegetation variability

Cited by 93 publications

References 65 publications

A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China

Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

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

Contact Info

Product

Resources

About