Quantifying the Response of German Forests to Drought Events via Satellite Imagery

Forest systems provide crucial ecosystem functions to our environment, such as balancing carbon stocks and influencing the local, regional and global climate. A trend towards an increasing frequency of climate change induced extreme weather events, including drought, is hereby a major challenge for forest management. Within this context, the application of remote sensing data provides a powerful means for fast, operational and inexpensive investigations over large spatial scales and time. This study was dedicated to explore the potential of satellite data in combination with harmonic analyses for quantifying the vegetation response to drought events in German forests. The harmonic modelling method was compared with a z-score standardization approach and correlated against both, meteorological and topographical data. Optical satellite imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS) was used in combination with three commonly applied vegetation indices. Highest correlation scores based on the harmonic modelling technique were computed for the 6th harmonic degree. MODIS imagery in combination with the Normalized Difference Vegetation Index (NDVI) generated hereby best results for measuring spectral response to drought conditions. Strongest correlation between remote sensing data and meteorological measures were observed for soil moisture and the self-calibrated Palmer Drought Severity Index (scPDSI). Furthermore, forests regions over sandy soils with pine as the dominant tree type were identified to be particularly vulnerable to drought. In addition, topographical analyses suggested mitigated drought affects along hill slopes. While the proposed approaches provide valuable information about vegetation dynamics as a response to meteorological weather conditions, standardized in-situ measurements over larger spatial scales and related to drought quantification are required for further in-depth quality assessment of the used methods and data.

[1]  Karolina Orłowska,et al.  Monitoring forest biodiversity and the impact of climate on forest environment using high-resolution satellite images , 2018 .

[2]  S. Mayr,et al.  Water stress limits transpiration and growth of European larch up to the lower subalpine belt in an inner‐alpine dry valley , 2018, The New phytologist.

[3]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[4]  S. Escuin,et al.  Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images , 2008 .

[5]  M. Battaglia,et al.  Density‐dependent vulnerability of forest ecosystems to drought , 2017 .

[6]  Zhiming Feng,et al.  Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors , 2013, Remote. Sens..

[7]  J. Storey,et al.  LANDSAT 7 SCAN LINE CORRECTOR-OFF GAP-FILLED PRODUCT DEVELOPMENT , 2005 .

[8]  A. Rigling,et al.  Drought response of five conifer species under contrasting water availability suggests high vulnerability of Norway spruce and European larch , 2013, Global change biology.

[9]  S. Netherer,et al.  Acute Drought Is an Important Driver of Bark Beetle Infestation in Austrian Norway Spruce Stands , 2019, Front. For. Glob. Change.

[10]  Christelle Vancutsem,et al.  Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia , 2018, Remote. Sens..

[11]  Juliane Huth,et al.  Earth Observation Based Monitoring of Forests in Germany: A Review , 2020, Remote. Sens..

[12]  Nicole M. Vaillant,et al.  Multi-temporal LiDAR and Landsat quantification of fire-induced changes to forest structure , 2017 .

[13]  F. Achard,et al.  Remote sensing of forest degradation in Southeast Asia—Aiming for a regional view through 5–30 m satellite data , 2014 .

[14]  Suming Jin,et al.  Comparison of time series tasseled cap wetness and the normalized difference moisture index in detecting forest disturbances , 2005 .

[15]  Wei Zhang,et al.  The relationship between NDVI and precipitation on the Tibetan Plateau , 2007 .

[16]  M. Manthey,et al.  Drought matters – Declining precipitation influences growth of Fagus sylvatica L. and Quercus robur L. in north-eastern Germany , 2011 .

[17]  Martha C. Anderson,et al.  Landsat-8: Science and Product Vision for Terrestrial Global Change Research , 2014 .

[18]  Eunmi Chang,et al.  NDVI-based land-cover change detection using harmonic analysis , 2015 .

[19]  Miroslav Svoboda,et al.  Forest disturbances under climate change. , 2017, Nature climate change.

[20]  M. Fenn,et al.  Nutrient status and plant growth effects of forest soils in the Basin of Mexico. , 2006, Environmental pollution.

[21]  F. Hao,et al.  Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River , 2012, Environmental Modeling & Assessment.

[22]  Zhe Zhu,et al.  Cloud detection algorithm comparison and validation for operational Landsat data products , 2017 .

[23]  J. Eitzinger,et al.  Spatio-temporal analysis of droughts in semi-arid regions by using meteorological drought indices. , 2013 .

[24]  David Frank,et al.  Tree-ring indicators of German summer drought over the last millennium , 2010 .

[25]  A. Rigling,et al.  Tree-growth analyses to estimate tree species' drought tolerance. , 2012, Tree physiology.

[26]  Dehai Zhu,et al.  Drought forecasting based on the remote sensing data using ARIMA models , 2010, Math. Comput. Model..

[27]  Shaun R. Levick,et al.  Exploring the Potential of C-Band SAR in Contributing to Burn Severity Mapping in Tropical Savanna , 2019, Remote. Sens..

[28]  V. Caselles,et al.  Mapping burns and natural reforestation using thematic Mapper data , 1991 .

[29]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[30]  M. Dobbertin,et al.  Tree growth in Swiss forests between 1995 and 2010 in relation to climate and stand conditions: Recent disturbances matter , 2014 .

[31]  D. Rigo,et al.  Picea abies in Europe: distribution, habitat, usage and threats , 2016 .

[32]  John F. Hermance,et al.  Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness , 2007 .

[33]  A. Viña,et al.  Drought Monitoring with NDVI-Based Standardized Vegetation Index , 2002 .

[34]  Qiuhong Tang,et al.  Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal , 2018, Forests.

[35]  David E. Knapp,et al.  Automated mapping of tropical deforestation and forest degradation: CLASlite , 2009 .

[36]  Matthias Bürgi,et al.  Climate change and nature conservation in Central European forests: a review of consequences, concepts and challenges , 2011 .

[37]  Julia A. Barsi,et al.  The next Landsat satellite: The Landsat Data Continuity Mission , 2012 .

[38]  Hankui K. Zhang,et al.  Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity. , 2016, Remote sensing of environment.

[39]  Ronald E. McRoberts,et al.  Harmonic regression of Landsat time series for modeling attributes from national forest inventory data , 2018 .

[40]  S. Goward,et al.  An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks , 2010 .

[41]  Markus Metz,et al.  Fourier transforms for detecting multitemporal landscape fragmentation by remote sensing , 2013 .

[42]  Jun Chen,et al.  Detection of Cropland Change Using Multi-Harmonic Based Phenological Trajectory Similarity , 2018, Remote. Sens..

[43]  Kenton Lee,et al.  The Spectral Response of the Landsat-8 Operational Land Imager , 2014, Remote. Sens..

[44]  J. Vogt,et al.  Climatic response and impacts of drought on oaks at urban and forest sites , 2013 .

[45]  R. Sadiq,et al.  A review of drought indices , 2011 .

[46]  Mia Hubert,et al.  Robust statistics for outlier detection , 2011, WIREs Data Mining Knowl. Discov..

[47]  Marco Heurich,et al.  In Situ/Remote Sensing Integration to Assess Forest Health - A Review , 2016, Remote. Sens..

[48]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[49]  Donato Morresi,et al.  Forest Spectral Recovery and Regeneration Dynamics in Stand-Replacing Wildfires of Central Apennines Derived from Landsat Time Series , 2019, Remote. Sens..

[50]  Hankui K. Zhang,et al.  Landsat 5 Thematic Mapper reflectance and NDVI 27-year time series inconsistencies due to satellite orbit change , 2016 .

[51]  Premysl Stych,et al.  Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks , 2019, ISPRS Int. J. Geo Inf..

[52]  Chelcy Ford Miniat,et al.  Topography may mitigate drought effects on vegetation along a hillslope gradient , 2016 .

[53]  Marco Heurich,et al.  Understanding Forest Health with Remote Sensing-Part II - A Review of Approaches and Data Models , 2017, Remote. Sens..

[54]  Xulin Guo,et al.  Compare NDVI extracted from Landsat 8 imagery with that from Landsat 7 imagery , 2014 .

[55]  Kenton Lee,et al.  Landsat 8 Operational Land Imager On-Orbit Geometric Calibration and Performance , 2014, Remote. Sens..

[56]  M. Parisien,et al.  Resistance of the boreal forest to high burn rates , 2014, Proceedings of the National Academy of Sciences.

[57]  D. Roy,et al.  A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin , 2008 .

[58]  Roland Geerken,et al.  An algorithm to classify and monitor seasonal variations in vegetation phenologies and their inter-annual change , 2009 .

[59]  A. Barbati,et al.  Climate change impacts, adaptive capacity, and vulnerability of European forest ecosystems , 2010 .

[60]  M. Herold,et al.  Near real-time disturbance detection using satellite image time series , 2012 .

[61]  Joan L. Walker,et al.  Using multi-spectral landsat imagery to examine forest health trends at Fort Benning, Georgia , 2016 .

[62]  T. Ficker,et al.  3D Image Reconstructions and the Nyquist–Shannon Theorem , 2015 .

[63]  F. Maselli Monitoring forest conditions in a protected Mediterranean coastal area by the analysis of multiyear NDVI data , 2004 .

[64]  S. Bruin,et al.  Analysis of monotonic greening and browning trends from global NDVI time-series , 2011 .

[65]  E. Johnson,et al.  The Relative Importance of Fuels and Weather on Fire Behavior in Subalpine Forests , 1995 .

[66]  Martin T. Sykes,et al.  Climate Change Impacts: Vegetation , 2009 .

[67]  Mathias Schardt,et al.  Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series , 2016, Remote. Sens..

[68]  B. Markham,et al.  Spectral characterization of the LANDSAT Thematic Mapper sensors , 1985 .

[69]  W. J. Wang,et al.  NDVI, temperature and precipitation changes and their relationships with different vegetation types during 1998–2007 in Inner Mongolia, China , 2013 .

[70]  Ian W. Housman,et al.  An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States , 2018, Remote. Sens..