Vegetation dynamics in a climate change hotspot: trend analysis in a Spanish dehesa

2021 ◽  
Author(s):  
Fabian Reddig ◽  
Georg Bareth ◽  
Christina Bogner
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Water Stress ◽  
Vegetation Dynamics ◽  
Data Driven ◽  
Seasonal Component ◽  
Positive Effects ◽  
Oak Trees ◽  
Starting Point ◽  
Ndvi Time Series

<p><strong>      Introduction </strong>The Mediterranean region has been identified as a hotspot of climate change characterized by a large tree mortality. Extended drought periods, shifts in rainfall patterns, and increasing water stress are probably the main drivers. Especially holm (<em>Quercus ilex L.</em>) and cork oak trees (<em>Quercus suber L.</em>) in high-value and nature-based agroforestry systems (in Spain known as dehesa) have multiple positive effects on the microclimate, carbon storage, erosion prevention, increase of soil water content, and soil nutrient concentration, for example. With their positive effect on wind velocity, they are also considered the last natural barrier protecting the Iberian Peninsula and Central Europe from desertification processes advancing from North Africa.<br><strong>     Objective </strong>We assume that wrong management, biotic causes like pests and diseases, and especially water stress are responsible for a decreased resilience of oak trees. Our goal was to analyse the vegetation dynamics with the help of the Normalized Difference Vegetation Index (NDVI) time series as an indicator for greenness and vitality. In particular, we focused on the trend of NDVI over about two decades.<br><strong>    Material and Methods</strong> We have selected eight plots (250 m x 250 m) with different topographical conditions and analysed an 18 years long NDVI time series (2003 - 2020) from MODIS (MYD13Q1). To extract the trend, we decomposed the time series into trend, seasonal component, and the high-frequency remainder. Subsequently, we did the Mann-Kendall test on the trend component to determine whether the trend is significant. Since environmental time series are rarely linear or stationary, many statistical decomposition methods are not suitable to produce physically meaningful results. Therefore we used the data-driven method <em>Complete Ensemble Empirical Mode Decomposition with adaptive Noise</em> (CEEMDAN) by Torres et al. 2011.<br><strong>     Results </strong>Depending on the topographical conditions of the plot, we were able to extract different NDVI trend signals from the time series. The NDVI values on the north-facing plots were larger than on the south-facing plots. The extracted trends were positive and significant (p <0.01). The seasonal component corresponded to the expected annual cycle.<br><strong>      Conclusion</strong> In order to assess vegetation dynamics, NDVI time series can be regarded as a good starting point, although one indicator alone does not allow to make final conclusions about vegetation changes. The purely data-driven decomposition method CEEMDAN avoids strong assumptions about the shape of the trend.</p>

scholarly journals Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e106613 ◽  
Author(s):  
Roberto O. Chávez ◽  
Jan G. P. W. Clevers ◽  
Jan Verbesselt ◽  
Paulette I. Naulin ◽  
Martin Herold
Keyword(s):  
Time Series ◽  
Water Stress ◽  
Stress Detection ◽  
New Approach ◽  
Ndvi Time Series

scholarly journals Are Soybean Yields Getting a Free Ride From Climate Change? Evidence From Argentine Time Series

2018 ◽  
Author(s):  
Hildegart Ahumada ◽  
Magdalena Cornejo
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Extreme Rainfall ◽  
Co2 Concentration ◽  
Interesting Case ◽  
Extreme Rainfall Events ◽  
Beneficial Effects ◽  
Positive Effects ◽  
Long Run ◽  
Equilibrium Correction

Soybean yields are often indicated as an interesting case of climate change mitigation due to the beneficial effects of CO2 fertilization. In this paper we econometrically study this effect using a time series model of yields in a multivariate framework for a main producer and exporter of this commodity, Argentina. We have to deal with the upward behavior of soybean yields trying to identify which variables are the long-run determinants responsible of its observed trend. With this aim we adopt a partial system approach to estimate subsets of long-run relationships due to climate, technological and economic factors. Using an automatic selection algorithm we evaluate encompassing of the different obtained equilibrium correction models. We found that only technological innovations due to new crop practices and the use of modified seeds explain soybean yield in the long run. Regarding short run determinants we found positive effects associated with the use of standard fertilizers and also from changes in atmospheric CO2 concentration which would suggest a mitigation effect from global warming. However, we also found negative climate effects from periods of droughts associated with La Niña episodes, high temperatures and extreme rainfall events during the growing season of the plant.

scholarly journals Human and Climate Effects on the Hamoun Wetlands

2019 ◽  
Vol 11 (3) ◽  
pp. 609-622 ◽  
Author(s):  
Saeideh Maleki ◽  
Saeid Soltani Koupaei ◽  
Alireza Soffianian ◽  
Sassan Saatchi ◽  
Saeid Pourmanafi ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Time Series ◽  
Water Stress ◽  
Rural Areas ◽  
Human Activities ◽  
Large Scale ◽  
Upstream Region ◽  
Ecosystem Recovery ◽  
Climate Effects

Abstract Negative impacts of climate change on ecosystems have been increasing, and both the intensification and the mitigation of these impacts are strongly linked with human activities. Management and reduction of human-induced disturbances on ecosystems can mitigate the effects of climate change and enhance the ecosystem recovery process. Here, we investigate coupled human and climate effects on the wetland ecosystem of the lower Helmand basin from 1977 to 2014. Using time series climate-variable data and land-use changes from Landsat time series imagery, we compared changes in ecosystem status between the upstream and downstream regions. Results show that despite a strong and prolonged drought in the region, the upstream region of the lower Helmand basin remained dominated by agriculture, causing severe water stress on the Hamoun wetlands downstream. The loss of available water in wetlands was followed by large-scale land abandonment in rural areas, migration to the cities, and increasing unemployment and economic hardship. Our results suggest that unsustainable land-use policies in the upstream region, combined with synergistic effects of human activities and climate in lower Helmand basin, have exacerbated the effects of water stress on local inhabitants in the downstream region.

scholarly journals Vegetation Dynamics and Phenological Shifts in Long-term NDVI Time Series in Inner Mongolia, China

2020 ◽  
Vol 54 (1) ◽  
pp. 101-112
Author(s):  
Zhe GONG ◽  
Kensuke KAWAMURA ◽  
Naoto ISHIKAWA ◽  
Masakazu GOTO ◽  
Wulan TUYA ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Dynamics ◽  
Inner Mongolia ◽  
Ndvi Time Series ◽  
Phenological Shifts

Visualization and quantification of NDVI dynamics in semiarid grasslands through recurrence tools.

2021 ◽  
Author(s):  
Andrés F. Almeida-Ñauñay ◽  
Ernesto Sanz ◽  
Miguel Quemada ◽  
Juan C. Losada ◽  
Rosa M. Benito ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Dynamics ◽  
Climate System ◽  
Recurrence Plots ◽  
Time Step ◽  
Temporal Scales ◽  
Temporal Behaviour ◽  
Semiarid Grassland ◽  
Temperature And Precipitation ◽  
Ndvi Time Series

<p>Grassland dynamics are constantly changing at a variety of spatial and temporal scales. Remote-sensing techniques are used to detect, identify, and monitor ecosystem changes at multi-temporal scales. Particularly, Normalized Difference Vegetation Index (NDVI)-based time series are important to obtain numerical observations related to vegetation dynamics.</p> <p>It is within this context that Recurrence Plots (RPs), Cross Recurrence Plots (CRPs) and Recurrence Quantification Analysis (RQA) offer new insight into the analysis of non-linear processes. Altogether, recurrence techniques could describe the whole dynamics of the system, explore its temporal behaviour, and quantify its structure through complexity measures. The goal of this study is to compute recurrence techniques to visualize and quantify the temporal dynamics of the semiarid grassland-climate system.</p> <p>In this work, we chose a semiarid grassland area in the centre of Spain, characterized by a Mediterranean climate. Multispectral images were composed for 8-days and they were acquired from MODIS TERRA (MOD09Q1.006) product from 2002 to 2018. Then, NDVI time-series was generated from four pixels with a spatial resolution of 250 x 250 m<sup>2</sup>. Temperature and precipitation time-series were obtained from a nearby meteorological station and transformed into an 8-day time step.</p> <p>Our results demonstrated that RPs showed the seasonality of the NDVI time-series. Furthermore, abrupt changes in NDVI time series were detected at specific times, implying that an atypical event occurred during that time. Temperature-NDVI CRPs showed a periodical pattern between them, on the other hand, precipitation-NDVI CRPs showed more erratic behaviour. We also found that a maximum lag between the two time-series could be detected through recurrence techniques. Overall, our findings suggest that temperature and precipitation present a dynamic complexity that is revealed in NDVI response. Therefore, RPs and CRPs are an alternative and complementary method to analyse and quantify non-stationary process, such as vegetation dynamics.</p> <p><strong>Reference</strong></p> <p>Almeida-Ñauñay, A. F., Benito, R. M., Quemada, M., Losada, J. C., & Tarquis, A. M. Complexity of the Vegetation-Climate System Through Data Analysis. In International Conference on Complex Networks and Their Applications. Springer, Cham., 609-619, 2020</p> <p><strong>Acknowledgements</strong></p> <p>The authors acknowledge support from Project No. PGC2018-093854-B-I00 of the Spanish Ministerio de Ciencia Innovación y Universidades of Spain and the funding from the Comunidad de Madrid (Spain), Structural Funds 2014-2020 512 (ERDF and ESF), through project AGRISOST-CM S2018/BAA-4330 and the financial support from Boosting Agricultural Insurance based on Earth Observation data - BEACON project under agreement Nº 821964, funded under H2020_EU, DT-SPACE-01-EO-2018-2020.</p> <p> </p>

scholarly journals Are Vegetation Dynamics Impacted from a Nuclear Disaster? The Case of Chernobyl Using Remotely Sensed NDVI and Land Cover Data

Land ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 433
Author(s):  
Alexandra Gemitzi
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Human Activities ◽  
Vegetation Dynamics ◽  
Positive Impact ◽  
Remotely Sensed ◽  
Exclusion Zone ◽  
Land Cover Types ◽  
Nuclear Disaster ◽  
Ndvi Time Series

There is a growing interest for scientists and society to acquire deep knowledge on the impacts from environmental disasters. The present work deals with the investigation of vegetation dynamics in the Chernobyl area, a place widely known for the devastating nuclear disaster on the 26th of April 1986. To unveil any possible long-term radiation effects on vegetation phenology, the remotely sensed normalized difference vegetation index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) was analyzed within the 30 km Exclusion Zone, where all human activities were ceased at that time and public access and inhabitation have been prohibited ever since. The analysis comprised applications of seasonal trend analysis using two techniques, i.e., pixel-wise NDVI time series and spatially averaged NDVI time series. Both techniques were applied in each one of the individual land cover types. To assess the existence of abnormal vegetation dynamics, the same analyses were conducted in two broader zones, i.e., from 30 to 60 km and from 60 to 90 km, away from Chernobyl area, where human activities were not substantially altered. Results of both analyses indicated that vegetation dynamics in the 30 km Exclusion Zone correspond to increasing plant productivity at a rate considerably higher than that of the other two examined zones. The outcome of the analyses presented herein attributes greening trends in the 30 km and the 30 to 60 km zones to a combination of climate, minimized human impact and a consequent prevalence of land cover types which seem to be well adapted to increased radioactivity. The vegetation greening trends observed in the third zone, i.e., the 90 km zone, are indicative of the combination of climate and increasing human activities. Results indicate the positive impact from the absence of human activities on vegetation dynamics as far as vegetation productivity and phenology are concerned in the 30 km Exclusion Zone, and to a lower extent in the 60 km zone. Furthermore, there is evidence that land cover changes evolve into the prevalence of woody vegetation in an area with increased levels of radioactivity.

scholarly journals Analysis of vegetation and land cover dynamics in north-western Morocco during the last decade using MODIS NDVI time series data

2011 ◽  
Vol 8 (11) ◽  
pp. 3359-3373 ◽  
Author(s):  
C. Höpfner ◽  
D. Scherer
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Dynamics ◽  
Time Series Data ◽  
Series Data ◽  
Land Cover Types ◽  
Modis Ndvi ◽  
Ndvi Time Series ◽  
Western Morocco ◽  
North Western

Abstract. Vegetation phenology as well as the current variability and dynamics of vegetation and land cover, including its climatic and human drivers, are examined in a region in north-western Morocco that is nearly 22 700 km2 big. A gapless time series of Normalized Differenced Vegetation Index (NDVI) composite raster data from 29 September 2000 to 29 September 2009 is utilised. The data have a spatial resolution of 250 m and were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The presented approach allows to compose and to analyse yearly land cover maps in a widely unknown region with scarce validated ground truth data by deriving phenological parameters. Results show that the high temporal resolution of 16 d is sufficient for (a) determining local land cover better than global land cover classifications of Plant Functional Types (PFT) and Global Land Cover 2000 (GLC2000) and (b) for drawing conclusions on vegetation dynamics and its drivers. Areas of stably classified land cover types (i.e. areas that did not change their land cover type) show climatically driven inter- and intra-annual variability with indicated influence of droughts. The presented approach to determine human-driven influence on vegetation dynamics caused by agriculture results in a more than ten times larger area compared with stably classified areas. Change detection based on yearly land cover maps shows a gain of high-productive vegetation (cropland) of about 259.3 km2. Statistically significant inter-annual trends in vegetation dynamics during the last decade could however not be discovered. A sequence of correlations was respectively carried out to extract the most important periods of rainfall responsible for the production of green biomass and for the extent of land cover types. Results show that mean daily precipitation from 1 October to 15 December has high correlation results (max. r2=0.85) on an intra-annual time scale to NDVI percentiles (50 %) of land cover types. Correlation results of mean daily precipitation from 16 September to 15 January and percentage of yearly classified area of each land cover type are medium up to high (max. r2=0.64). In all, an offset of nearly 1.5 months is detected between precipitation rates and NDVI values. High-productive vegetation (cropland) is proved to be mainly rain-fed. We conclude that identification, understanding and knowledge about vegetation phenology, and current variability of vegetation and land cover, as well as prediction methods of land cover change, can be improved using multi-year MODIS NDVI time series data. This study enhances the comprehension of current land surface dynamics and variability of vegetation and land cover in north-western Morocco. It especially offers a quick access when estimating the extent of agricultural lands.

scholarly journals Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

2022 ◽  
Vol 14 (1) ◽  
pp. 582
Author(s):  
Shengxin Lan ◽  
Zuoji Dong
Keyword(s):  
Time Series ◽  
Vegetation Dynamics ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Vegetation Type ◽  
Sparse Vegetation ◽  
Type Transformation ◽  
Ndvi Time Series ◽  
The Mean ◽  
The Relationship

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

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