Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively

Impact of STARFM on Crop Yield Predictions: Fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany

Rapid and accurate yield estimates at both field and regional levels remain the goal of sustainable agriculture and food security. Hereby, the identification of consistent and reliable methodologies providing accurate yield predictions is one of the hot topics in agricultural research. This study investigated the relationship of spatiotemporal fusion modelling using STRAFM on crop yield prediction for winter wheat (WW) and oil-seed rape (OSR) using a semi-empirical light use efficiency (LUE) model for the Free State of Bavaria (70,550 km2), Germany, from 2001 to 2019. A synthetic normalised difference vegetation index (NDVI) time series was generated and validated by fusing the high spatial resolution (30 m, 16 days) Landsat 5 Thematic Mapper (TM) (2001 to 2012), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (2012), and Landsat 8 Operational Land Imager (OLI) (2013 to 2019) with the coarse resolution of MOD13Q1 (250 m, 16 days) from 2001 to 2019. Except for some temporal periods (i.e., 2001, 2002, and 2012), the study obtained an R2 of more than 0.65 and a RMSE of less than 0.11, which proves that the Landsat 8 OLI fused products are of higher accuracy than the Landsat 5 TM products. Moreover, the accuracies of the NDVI fusion data have been found to correlate with the total number of available Landsat scenes every year (N), with a correlation coefficient (R) of +0.83 (between R2 of yearly synthetic NDVIs and N) and −0.84 (between RMSEs and N). For crop yield prediction, the synthetic NDVI time series and climate elements (such as minimum temperature, maximum temperature, relative humidity, evaporation, transpiration, and solar radiation) are inputted to the LUE model, resulting in an average R2 of 0.75 (WW) and 0.73 (OSR), and RMSEs of 4.33 dt/ha and 2.19 dt/ha. The yield prediction results prove the consistency and stability of the LUE model for yield estimation. Using the LUE model, accurate crop yield predictions were obtained for WW (R2 = 0.88) and OSR (R2 = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R2 of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively

Disentangling effects of climate and land use on biodiversity and ecosystem services - a multi‐scale experimental design

Climate and land-use change are key drivers of environmental degradation in the Anthropocene, but too little is known about their interactive effects on biodiversity and ecosystem services. Long-term data on biodiversity trends are currently lacking. Furthermore, previous ecological studies have rarely considered climate and land use in a joint design, did not achieve variable independence or lost statistical power by not covering the full range of environmental gradients. Here, we introduce a multi-scale space-for-time study design to disentangle effects of climate and land use on biodiversity and ecosystem services. The site selection approach coupled extensive GIS-based exploration (i.e. using a Geographic information system) and correlation heatmaps with a crossed and nested design covering regional, landscape and local scales. Its implementation in Bavaria (Germany) resulted in a set of study plots that maximise the potential range and independence of environmental variables at different spatial scales. Stratifying the state of Bavaria into five climate zones (reference period 1981–2010) and three prevailing land-use types, that is, near-natural, agriculture and urban, resulted in 60 study regions (5.8 × 5.8 km quadrants) covering a mean annual temperature gradient of 5.6–9.8°C and a spatial extent of ~310 × 310 km. Within these regions, we nested 180 study plots located in contrasting local land-use types, that is, forests, grasslands, arable land or settlement (local climate gradient 4.5–10°C). This approach achieved low correlations between climate and land use (proportional cover) at the regional and landscape scale with |r ≤ 0.33| and |r ≤ 0.29| respectively. Furthermore, using correlation heatmaps for local plot selection reduced potentially confounding relationships between landscape composition and configuration for plots located in forests, arable land and settlements. The suggested design expands upon previous research in covering a significant range of environmental gradients and including a diversity of dominant land-use types at different scales within different climatic contexts. It allows independent assessment of the relative contribution of multi-scale climate and land use on biodiversity and ecosystem services. Understanding potential interdependencies among global change drivers is essential to develop effective restoration and mitigation strategies against biodiversity decline, especially in expectation of future climatic changes. Importantly, this study also provides a baseline for long-term ecological monitoring programs

Forschung aus dem All - Das Potenzial der Fernerkundung

Der Vortrag zeigt das Potenzial der Erdbeobachtung zur Gewinnung von Umweltinformationen. Anhand verschiedenster Beispiele aus der Forschung des Lehrstuhls für Fernerkundung und dem Deutschen Fernerkundungsdatenzentrum wird der Beitrag der Fernerkundung für Fragestellungen des Globalen Wandels aufgezeigt. Die gewonnenen Informationen erlauben die Identifikation und Erklärung räumlicher und zeitlicher Muster und die Erarbeitung von nachhaltigen Lösungsstrategien

Statistische Analyse von Standortfaktoren zur Erklärung von aus Fernerkundungsdaten abgeleiteten phänologischen Zeitpunkten am Beispiel von Grünlandflächen im Alpenvorland

"Der anhaltendende Wandel des Klimas der vergangenen Jahrzehnte spiegelt sich in einer erhöhten Variabilität phänologischer Phasen wieder. Studien zeigen, dass sich der Übergang von Frühling zu Sommer von 1971 bis 2000 im europäischen Raum um durchschnittlich 2,5 Tage pro Dekade früher ereignet hat. Die Fernerkundung ermöglicht es, solche Veränderungen flächenhaft zu untersuchen. Inte-ressant ist hierbei vor allem die Analytik einflussgebender Faktoren auf die ermittelten phänologischen Zeitpunkte, um die sich ergebenden Raummuster aus FE-Daten besser zu verstehen. Theoretisch ließen sich aus sich ändernden Standortfaktoren bis zu einem gewissen Grad Erwartungswerte für die zukünftige Entwicklung von Phänologie und damit der Wachstumsbedingungen an einem Standort be-stimmen. Durch Eigenschaften wie die Heterogenität aufgrund der topographischen Gradienten, die Variabilität in Schneebedeckung und -dauer sowie die Abwechslung anthropogener Nutzungseinheiten und Natur-schutzgebieten ist das Alpenvorland als Untersuchungsgebiet für vegetationskundliche Fragestellungen von besonderem Interesse. Die Vielzahl unterschiedlicher Standortbedingungen für Pflanzengesell-schaften erlaubt eine detaillierte Untersuchung der Einflussgrößen auf den jahreszeitlichen Verlauf der Vegetation. Als antreibende Faktoren der Phänologie weist die Forschung in der Klimazone der gemä-ßigten Breiten Temperatur, Niederschläge (vor allem als Schnee) und die Topographie nach. Ziel dieser Arbeit ist, diese erklärenden Variablen aus Fernerkundungsdaten und Daten des Deutschen Wetter-dienstes zu erstellen, ihren Einfluss auf die Phänologie statistisch abzuleiten und Wachstumsstadien für das Untersuchungsgebiet zu prognostizieren. Dabei standen als zu erklärende Variablen der Beginn (Start-Of-Season, SOS) und das Maximum der Vegetationsperiode (Day-Of-Peak, DOP) in den Jahren 2011 und 2012 aus RapidEye-Zeitserien über ein großes Untersuchungsgebiet zur Verfügung. Die Indikatoren wurden für den Untersuchungszeit-raum aus MODIS- (500 bzw. 1 km Auflösung), DWD- (1 km Auflösung) und SRTM-Daten (90 m Auflösung) abgeleitet und beschreiben topographische Aspekte (z.B. topogr. Höhe, Hangneigung, Hangausrichtung) und ausgewählte klimatologische Messgrößen (z.B. Anzahl der Schneetage, minimale Temperatur). Die statistische Analyse wurde anhand von „Classification and Regression Trees“ (CART) durchgeführt. Dabei wurden verschiedene Eingabeparameter in Betracht gezogen, um die erstellten Indikatoren auf deren Wichtigkeit hin zu überprüfen und Regeln für eine anschließende flächenhafte Vorhersage festzulegen. Erste Ergebnisse der Untersuchung zeigen, dass die Einflussgrößen in Form von Indikatoren aus fern-erkundlichen Datensätzen bis zu einem bestimmten Grad reproduziert werden können. Zwar ergeben sich nur geringe Korrelationen zwischen den Werten des DOP und der einzelnen Indikatoren (Ø R2=0,05), doch das Bestimmtheitsmaß (R2) zwischen vorhergesagtem und originalem DOPs liegt je nach angewandten Einstellungen mit CART bislang zwischen 0,06 und 0,4. In diesem Modell zeigen v.a. die Indikatoren der topographische Höhe und die Schneeindikatoren einen erhöhten Einfluss auf die Vorhersage des DOP. . Auf Basis dieser vielversprechenden Ergebnisse werden aktuell weitere Indikatoren, ein komplexerer Algorithmus „Random Forests“ (RF), und die Skalenniveaus der Untersuchung geprüft, sowie temporal besser aufgelöste Produkte in die Untersuchung eingebunden.

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