The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R2 = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R2 = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R2 = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R2 = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R2 = 0.60, RMSE = 0.05) and S-MOD13Q1 (R2 = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution.
1. 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.2. 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. 3. 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 | 515 Methods in Ecology and Evoluঞon REDLICH Et aL.
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.
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.
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