Fernerkundung der Vegetationsphänologie über MODIS NDVI Daten - Herausforderungen bei der Datenverarbeitung und -validierung mittels Bodenbeobachtungen zahlreicher Arten und LiDAR

Phenology, the cyclic events in living organisms is triggered by climatic conditions and indicators of climate change. They are important factors influencing species interactions and ecosystem functioning. This thesis deals with the estimation of phenological metrics (Land Surface Phenology or LSP) from MODIS based time series NDVI data. Results of data analysis emphasises the role of ground observations, topography and LiDAR characteristics of forest stand in describing the variability in LSP.Phänologie, die zyklischen Stadien von lebenden Organismen werden über klimatische Verhältnisse gesteuert und dienen als Indikatoren des Klimawandels. Diese Faktoren beeinflussen maßgeblich die Interaktionen zwischen Arten und sind für das Funktionieren von Ökosystemen ausschlaggebend. Diese Arbeit behandelt die Bestimmung von phänologischen Metriken (Phänologie der Landoberfläche oder LSP) unter Verwendung von MODIS basierten NDVI Zeitreihen. Die Ergebnisse der Datenanalyse hebt die Wichtigkeit von Bodenbeobachtungen, Topographie und LiDAR Merkmalen von Waldbeständen bei der Beschreibung der LSP Variabilität hervor

Effects of Different Methods on the Comparison between Land Surface and Ground Phenology—A Methodological Case Study from South-Western Germany

Several methods exist for extracting plant phenological information from time series of satellite data. However, there have been only a few successful attempts to temporarily match satellite observations (Land Surface Phenology or LSP) with ground based phenological observations (Ground Phenology or GP). The classical pixel to point matching problem along with the temporal and spatial resolution of remote sensing data are some of the many issues encountered. In this study, MODIS-sensor’s Normalised Differenced Vegetation Index (NDVI) time series data were smoothed using two filtering techniques for comparison. Several start of season (SOS) methods established in the literature, namely thresholds of amplitude, derivatives and delayed moving average, were tested for determination of LSP-SOS for broadleaf forests at a site in southwestern Germany using 2001–2013 time series of NDVI data. The different LSP-SOS estimates when compared with species-rich GP dataset revealed that different LSP-SOS extraction methods agree better with specific phases of GP, and the choice of data processing or smoothing strongly affects the LSP-SOS extracted. LSP methods mirroring late SOS dates, i.e., 75% amplitude and 1st derivative, indicated a better match in means and trends, and high, significant correlations of up to 0.7 with leaf unfolding and greening of late understory and broadleaf tree species. GP-SOS of early understory leaf unfolding partly were significantly correlated with earlier detecting LSP-SOS, i.e., 20% amplitude and 3rd derivative. Early understory SOS were, however, more difficult to detect from NDVI due to the lack of a high resolution land cover information

Ground and satellite phenology in alpine forests are becoming more heterogeneous across higher elevations with warming

The role of temperature as a key driver for plant phenology is well established. However, an increasing lack of winter chilling may strongly slow down spring phenological advances in the course of warming. Along elevational gradients in the mountains, differential changes in winter chilling as well as more homogeneous leaf unfolding due to warming have been observed. In this study, we analyzed the elevation-linked lapse rates of phenological ground observations and remote sensing data in the pre-alpine and alpine regions of the Bavarian Alps, Germany. Seasonal start (SOS) and end of season (EOS) dates were extracted from time series data of 4-day maximum value composite Moderate Resolution Imaging Spectrometer (MODIS) sensor's Normalized Difference Vegetation Index (NDVI) for the years 2001-2016. Longer snow duration in the alpine region could be a possible reason of higher SOS elevational lapse rates as compared to the pre-alpine region. Significant and maximum differences in SOS rates between alpine and pre-alpine areas were observed in years with preceding warm winters with insufficient chilling. Minimum differences in SOS elevational lapse rates along the elevational gradients were found for cold spring and cold winter years. The MODIS-based SOS showed the highest correspondence when validated against the gridded German Meteorological Service (DWD) leaf unfolding data. However, EOS dates were in comparatively lower agreement with DWD data, and their lapse rates in the pre-alpine and alpine regions were difficult to validate. Contrary to the SOS, lower positive lapse rates of EOS were revealed in the alpine but not in the pre-alpine areas

Status of Phenological Research Using Sentinel-2 Data: A Review

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments

LiDAR derived topography and forest stand characteristics largely explain the spatial variability observed in MODIS land surface phenology

In the past, studies have successfully identified climatic controls on the temporal variability of the land surface phenology (LSP). Yet we lack a deeper understanding of the spatial variability observed in LSP within a land cover type and the factors that control it. Here we make use of a high resolution LiDAR based dataset to study the effect of subpixel forest stand characteristics on the spatial variability of LSP metrics based on MODIS NDVI. Multiple linear regression techniques (MLR) were applied on forest stand information and topography derived from LiDAR as well as land cover information (i.e. CORINE and proprietary habitat maps for the year 2012) to predict average LSP metrics of the mountainous Bavarian Forest National Park, Germany. Six different LSP metrics, i.e. start of season (SOS), end of season (EOS), length of season (LOS), NDVI integrated over the growing season (NDVIsum), maximum NDVI value (NDVImax) and day of maximum NDVI (maxDOY) were modelled in this study. It was found that irrespective of the land cover, the mean SOS, LOS and NDVIsum were largely driven by elevation. However, inclusion of detailed forest stand information improved the models considerably. The EOS however was more complex to model, and the subpixel percentage of broadleaf forests and the slope of the terrain were found to be more strongly linked to EOS. The explained variance of the NDVImax improved from 0.45 to 0.71 when additionally considering land cover information, which further improved to 0.84 when including LiDAR based subpixelforest stand characteristics. Since completely homogenous pixels are rare in nature, our results suggest that incorporation of subpixel forest stand information along with land cover type leads to an improved performance of topography based LSP models. The novelty of this study lies in the use of topography, land cover and subpixel vegetation characteristics derived from LiDAR in a stepwise manner with increasing level of complexity, which demonstrates the importance of forest stand information on LSP at the pixel level.</p

Evaluating the effectiveness of water infrastructures for increasing groundwater recharge and agricultural production – a case study of Gujarat, India

Groundwater for agricultural purposes is of utmost importance in the Indian state of Gujarat. To augment existing agricultural water resources, the Gujarat Government invested in many large-scale and smallscale water infrastructures (WI). In order to increase water storage and groundwater recharge and to justify further investments in WI, a better understanding on the impacts of past WIs is needed. This study uses data from NASA’s Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems, to estimate water storage before and after the intensification in the investment in WIs. In addition, Normalised Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectrometer (MODIS) sensor was used to show changes in seasonal cropped areas during the same period. The analysis of data showed that the water storage in the state was estimated to be 24 BCM in 2003–2004 and 30 BCM in 2010–2011, an increase of 29% pre and post WI intensification. The Pixel Crop Duration Index (PCDI) indicated an increase in cropped area (at district level) in 2010–2011 when compared with 2003–2004 period, by 30% on yearly basis and about 80% during non-monsoon period. Results also indicates a significant net increase in water storage (by 5890 M m3 after water used for crop intensification) and increase in agricultural crop area (by 63,862 km2) in Gujarat during the period of intensification in infrastructures for water storage and groundwater recharge. Results also indicate that some districts have higher net water storage (compared to 2003), however the cropped area duration - PCDI has not increased much (e.g., Valsad and Navsari). The findings of this study can increase the understanding of the potential of WIs and provide valuable guidance for increasing cropped area in high water storage regions of Gujarat

Evaluating the effectiveness of water infrastructures for increasing groundwater recharge and agricultural production : a case study of Gujarat, India

Groundwater for agricultural purposes is of utmost importance in the Indian state of Gujarat. To augment existing agricultural water resources, the Gujarat Government invested in many large-scale and small-scale water infrastructures (WI). In order to increase water storage and groundwater recharge and to justify further investments in WI, a better understanding on the impacts of past WIs is needed. This study uses data from NASA's Gravity Recovery and Climate Experiment (GRACE), along with soil moisture data from the Global Land Data Assimilation Systems, to estimate water storage before and after the intensification in the investment in WIs. In addition, Normalised Difference Vegetation Index (NDVI) data from the Moderate Resolution Imaging Spectrometer (MODIS) sensor was used to show changes in seasonal cropped areas during the same period. The analysis of data showed that the water storage in the state was estimated to be 24 BCM in 2003–2004 and 30 BCM in 2010–2011, an increase of 29% pre and post WI intensification. The Pixel Crop Duration Index (PCDI) indicated an increase in cropped area (at district level) in 2010–2011 when compared with 2003–2004 period, by 30% on yearly basis and about 80% during non-monsoon period. Results also indicates a significant net increase in water storage (by 5890 M m3 after water used for crop intensification) and increase in agricultural crop area (by 63,862 km2) in Gujarat during the period of intensification in infrastructures for water storage and groundwater recharge. Results also indicate that some districts have higher net water storage (compared to 2003), however the cropped area duration - PCDI has not increased much (e.g., Valsad and Navsari). The findings of this study can increase the understanding of the potential of WIs and provide valuable guidance for increasing cropped area in high water storage regions of Gujarat

Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

Phenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year⁻¹ for spring and summer, while late autumn and winter showed a delay of around 0.1 days year⁻¹. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes