TY  - JOUR
A1  - Philipp, Marius
A1  - Wegmann, Martin
A1  - Kübert-Flock, Carina
T1  - Quantifying the Response of German Forests to Drought Events via Satellite Imagery
JF  - Remote Sensing
N2  - 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.
KW  - time-series
KW  - harmonic analysis
KW  - z-score
KW  - scPDSI
KW  - drought
KW  - vegetation response
KW  - forest ecosystems
KW  - Google Earth Engine
Y1  - 2021
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-239575
SN  - 2072-4292
VL  - 13
IS  - 9
ER  - 
TY  - JOUR
A1  - Nguyen, Duy Ba
A1  - Kersten, Clauss
A1  - Senmao, Cao
A1  - Vahid, Naeimi
A1  - Kuenzer, Claudia
A1  - Wagner, Wolfgang
T1  - Mapping Rice Seasonality in the Mekong Delta with Multi-Year Envisat ASAR WSM Data
JF  - Remote Sensing
N2  - Rice is the most important food crop in Asia, and the timely mapping and monitoring of paddy rice fields subsequently emerged as an important task in the context of food security and modelling of greenhouse gas emissions. Rice growth has a distinct influence on Synthetic Aperture Radar (SAR) backscatter images, and time-series analysis of C-band images has been successfully employed to map rice fields. The poor data availability on regional scales is a major drawback of this method. We devised an approach to classify paddy rice with the use of all available Envisat ASAR WSM (Advanced Synthetic Aperture Radar Wide Swath Mode) data for our study area, the Mekong Delta in Vietnam. We used regression-based incidence angle normalization and temporal averaging to combine acquisitions from multiple tracks and years. A crop phenology-based classifier has been applied to this time series to detect single-, double- and triple-cropped rice areas (one to three harvests per year), as well as dates and lengths of growing seasons. Our classification has an overall accuracy of 85.3% and a kappa coefficient of 0.74 compared to a reference dataset and correlates highly with official rice area statistics at the provincial level (R-2 of 0.98). SAR-based time-series analysis allows accurate mapping and monitoring of rice areas even under adverse atmospheric conditions.
KW  - band SAR data
KW  - SAR
KW  - rice
KW  - WSM
KW  - ASAR
KW  - Envisat
KW  - MODIS image
KW  - Southeast China
KW  - polarimetric SAR
KW  - cropping systems
KW  - time-series
KW  - paddy rice
KW  - radar
KW  - paddy
KW  - rice mapping
KW  - Vietnam
KW  - Mekong-Delta
KW  - synthetic aperture radar
KW  - multitemporal ALOS/PALSAR imagery
KW  - soil moisture retrieval
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-137554
VL  - 7
IS  - 12
ER  -