The impact of climate change on semi-natural meadows in Northern Portugal - A time-frequency analysis Mario Cunha, University of Porto and Christian Richter, University of East London

1 Introduction Permanent semi-natural grassland meadows, locally are essential elements of the mountain rural landscape in Northeast Portugal. They represent the main fodder resource for the livestock production, which is the main economical input to the local farmers. They are also recognized for their impact on the landscape and several ecological services related with natural resources conservation 24/05/2019 Christian Richter

1 Introduction the increased water constrains due to sectoral competition for water-uses and the foreseeable climate change endanger the sustainability of these meadows. These developments may result in the deterioration of several ecological services and eventually a loss of this cultural landscape. 24/05/2019 Christian Richter

1 Introduction To preserve these meadows, it is essential to gather useful information for the sustainable management of semi-natural meadows ecosystem and grazing activities. Hence, modelling the vegetation dynamics responses to the inter-annual climate variability could help on decisions relative to conservation strategies. Field surveys of vegetation dynamics monitoring, related with management practices and climate, although useful are difficult and time-consuming. 24/05/2019 Christian Richter

1 Introduction A number of Earth Observing System (EOS) sensors currently offer low spatial resolution images with a high revisiting rate (or high temporal resolution), such as the VEGETATION sensor, on board the most recent Satellite Pour l’Observation de la Terre (SPOT) satellite. This sensor monitors vegetation in agricultural areas for intermediate spatial resolution (1 km), and high temporal resolution (10 days synthesis products). Time-series of satellite imagery can provide a synoptic view of vegetation dynamics by measuring surface reflectance. Previous studies have analysed annual vegetation growth of these semi-natural meadows using satellite data (Cunha et al. 2010; Poças et al. 2011). 24/05/2019 Christian Richter

1 Introduction Remote sensing vegetation monitoring is frequently based on so-called vegetation indices that are combinations of spectral measurements in different wavelengths as recorded by a radiometric sensor. They aid in the analysis of multi-spectral image information by shrinking multi-dimensional data into a single value. In the past few years many vegetation indices extracted from hyperspectral satellite imagery have been tested for evaluating vegetation growth, but the Normalized Difference Vegetation Index (NDVI) is still the most popular (Rouse et al. 1973): 24/05/2019 Christian Richter

1 Introduction where ρNIR and ρred are, the surface reflectance in the near-infrared and in the red channels. Detecting changes in NDVI time series data is not straightforward, since they contain a combination of seasonal and trend, in addition to noise that originates from remnant geometric errors, atmospheric scatter and cloud effects. In order to analyse satellite imagery, several pre-processing tasks are need. 24/05/2019 Christian Richter

1 Introduction Problems: stationarity assumptions, data quality, sensor noise and complexity of the methods can make it a challenge to quantify the separate sources of information that influence the signal and to determine what constitutes a significant change. 24/05/2019 Christian Richter

1 Introduction Study area: 24/05/2019 Christian Richter

1 Introduction The region of interest covers a large area of the mountain region of Montalegre, Northeast Portugal. 24/05/2019 Christian Richter

2 Data Ten days NDVI synthesis (S10-composite) satellite images dataset from SPOT-VEGETATION (VGT) are used to produce temporal NDVI profiles for the test site located in PRR. The 10 days annual cycles between April 1998 and March 2011 were used for analysis (474 images layers; 36 images from each year). The S10 composites are corrected for radiometric, geometric and atmospheric effects. 24/05/2019 Christian Richter

2 Data Meteorological observations for the years 1998 to 2011 were taken from the weather station of Montalegre (41º49’N: 7º47’W: 1005m of elevation) located in the proximity of the test site. The meteorological data consist of daily observations of maximum and minimum temperature and precipitation (R). These general meteorological parameters were used to derive other variables: mean temperature (Tm, ºC), Potential Evapotranspiration (ETP, mm) and other variables related with soil water balance. 24/05/2019 Christian Richter

2 Data 24/05/2019 Christian Richter

2 Data Figure 2: Spring Growth of NDVI 2- Year cycle changed into 4 year cycle! Figure 2: Spring Growth of NDVI 24/05/2019 Christian Richter

2 Data Figure 3: Spring Temperature 24/05/2019 Christian Richter

2 Data Figure 4: Growth and Temperature 24/05/2019 Christian Richter

2 Data Figure 5: Growth and Soil Water 24/05/2019 Christian Richter

3 Methodology How we do it: We estimate each growth rate individually using an AR(X) specification. This AR(X) specification is time-varying. For each point in time we calculate the Fourier transform. That gives us the time-varying spectrum. This step allows us already to highlight differences in the growth rate. 24/05/2019 Christian Richter

3 Methodology The coherence is defined as: We then estimate the link between two variables using the Kalman filter. This step results in a time-varying gain. Given the individual spectra and the gain, we can now calculate the coherence. This coherence is also time-varying. The coherence is defined as: 24/05/2019 Christian Richter

4 Results Here we estimate the following relationship: As a first step we analyse the power spectral density function of PPR region. The power spectral density function (PSD) shows the strength of the variations (energy) of a time series at each frequency of oscillation. In other words, it decomposes the variance of a time series into its periodicities. 24/05/2019 Christian Richter

4 Results In a diagram it shows at which frequencies variations are strong/powerful, and at which frequencies the variations are weak (expressed in “energy”). The unit of measurement in the PSD is energy (variance) per frequency, frequency band or cycle length. For example, if a time series and constant over time, the power spectrum would look like the following figure 24/05/2019 Christian Richter

4 Results Energy σ2 Frequency () Figure 1: Hypothetical Spectrum 24/05/2019 Christian Richter

4 Results Figure 2: Time-Varying Spectrum of NDVI 24/05/2019 Christian Richter

4 Results We then estimate: We can then calculate the coherence: 24/05/2019 Christian Richter

4 Results Figure 3: Coherence between NDVI and Temperature 24/05/2019 Christian Richter

4 Results Figure 4: Coherence between NDVI and Soil Water 24/05/2019 Christian Richter

5 Conclusion The NDVI spectrum shows that vegetation does not always follow seasons to the same extent. (favourable) temperature always played a large role for vegetation across the season, but the impact is decreasing lately. Given the lack of rainfall, the ability of the soil to store water becomes more important. 24/05/2019 Christian Richter

5 Conclusion Question is whether with rising temperatures the soil remains fertile or silts up. Investment in infrastructure may be needed to keep soil fertile. 24/05/2019 Christian Richter