@article{DhillonDahmsKuebertFlocketal.2023, author = {Dhillon, Maninder Singh and Dahms, Thorsten and Kuebert-Flock, Carina and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias}, title = {Integrating random forest and crop modeling improves the crop yield prediction of winter wheat and oil seed rape}, series = {Frontiers in Remote Sensing}, volume = {3}, journal = {Frontiers in Remote Sensing}, issn = {2673-6187}, doi = {10.3389/frsen.2022.1010978}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-301462}, year = {2023}, abstract = {The fast and accurate yield estimates with the increasing availability and variety of global satellite products and the rapid development of new algorithms remain a goal for precision agriculture and food security. However, the consistency and reliability of suitable methodologies that provide accurate crop yield outcomes still need to be explored. The study investigates the coupling of crop modeling and machine learning (ML) to improve the yield prediction of winter wheat (WW) and oil seed rape (OSR) and provides examples for the Free State of Bavaria (70,550 km2), Germany, in 2019. The main objectives are to find whether a coupling approach [Light Use Efficiency (LUE) + Random Forest (RF)] would result in better and more accurate yield predictions compared to results provided with other models not using the LUE. Four different RF models [RF1 (input: Normalized Difference Vegetation Index (NDVI)), RF2 (input: climate variables), RF3 (input: NDVI + climate variables), RF4 (input: LUE generated biomass + climate variables)], and one semi-empiric LUE model were designed with different input requirements to find the best predictors of crop monitoring. The results indicate that the individual use of the NDVI (in RF1) and the climate variables (in RF2) could not be the most accurate, reliable, and precise solution for crop monitoring; however, their combined use (in RF3) resulted in higher accuracies. Notably, the study suggested the coupling of the LUE model variables to the RF4 model can reduce the relative root mean square error (RRMSE) from -8\% (WW) and -1.6\% (OSR) and increase the R 2 by 14.3\% (for both WW and OSR), compared to results just relying on LUE. Moreover, the research compares models yield outputs by inputting three different spatial inputs: Sentinel-2(S)-MOD13Q1 (10 m), Landsat (L)-MOD13Q1 (30 m), and MOD13Q1 (MODIS) (250 m). The S-MOD13Q1 data has relatively improved the performance of models with higher mean R 2 [0.80 (WW), 0.69 (OSR)], and lower RRMSE (\%) (9.18, 10.21) compared to L-MOD13Q1 (30 m) and MOD13Q1 (250 m). Satellite-based crop biomass, solar radiation, and temperature are found to be the most influential variables in the yield prediction of both crops.}, language = {en} } @article{DhillonKuebertFlockDahmsetal.2023, author = {Dhillon, Maninder Singh and K{\"u}bert-Flock, Carina and Dahms, Thorsten and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias}, title = {Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {7}, issn = {2072-4292}, doi = {10.3390/rs15071830}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311132}, year = {2023}, abstract = {The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products' high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17\%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35\%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.}, language = {en} } @article{EnglmeierMitesserBenbowetal.2023, author = {Englmeier, Jana and Mitesser, Oliver and Benbow, M. Eric and Hothorn, Torsten and von Hoermann, Christian and Benjamin, Caryl and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Redlich, Sarah and Riebl, Rebekka and Rojas Botero, Sandra and Rummler, Thomas and Steffan-Dewenter, Ingolf and Stengel, Elisa and Tobisch, Cynthia and Uhler, Johannes and Uphus, Lars and Zhang, Jie and M{\"u}ller, J{\"o}rg}, title = {Diverse effects of climate, land use, and insects on dung and carrion decomposition}, series = {Ecosystems}, volume = {26}, journal = {Ecosystems}, number = {2}, issn = {1432-9840}, doi = {10.1007/s10021-022-00764-7}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-325064}, pages = {397-411}, year = {2023}, abstract = {Land-use intensification and climate change threaten ecosystem functions. A fundamental, yet often overlooked, function is decomposition of necromass. The direct and indirect anthropogenic effects on decomposition, however, are poorly understood. We measured decomposition of two contrasting types of necromass, rat carrion and bison dung, on 179 study sites in Central Europe across an elevational climate gradient of 168-1122 m a.s.l. and within both local and regional land uses. Local land-use types included forest, grassland, arable fields, and settlements and were embedded in three regional land-use types (near-natural, agricultural, and urban). The effects of insects on decomposition were quantified by experimental exclusion, while controlling for removal by vertebrates. We used generalized additive mixed models to evaluate dung weight loss and carrion decay rate along elevation and across regional and local land-use types. We observed a unimodal relationship of dung decomposition with elevation, where greatest weight loss occurred between 600 and 700 m, but no effects of local temperature, land use, or insects. In contrast to dung, carrion decomposition was continuously faster with both increasing elevation and local temperature. Carrion reached the final decomposition stage six days earlier when insect access was allowed, and this did not depend on land-use effect. Our experiment identified different major drivers of decomposition on each necromass form. The results show that dung and carrion decomposition are rather robust to local and regional land use, but future climate change and decline of insects could alter decomposition processes and the self-regulation of ecosystems.}, language = {en} } @article{DhillonDahmsKuebertFlocketal.2023, author = {Dhillon, Maninder Singh and Dahms, Thorsten and K{\"u}bert-Flock, Carina and Liepa, Adomas and Rummler, Thomas and Arnault, Joel and Steffan-Dewenter, Ingolf and Ullmann, Tobias}, title = {Impact of STARFM on crop yield predictions: fusing MODIS with Landsat 5, 7, and 8 NDVIs in Bavaria Germany}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {6}, issn = {2072-4292}, doi = {10.3390/rs15061651}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-311092}, year = {2023}, abstract = {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 km\(^2\)), 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 R\(^2\) 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 R\(^2\) 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 R\(^2\) 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 (R\(^2\) = 0.88) and OSR (R\(^2\) = 0.74). Lastly, the study observed a high positive correlation of R = 0.81 and R = 0.77 between the yearly R\(^2\) of synthetic accuracy and modelled yield accuracy for WW and OSR, respectively.}, language = {en} } @article{EnglmeiervonHoermannRiekeretal.2022, author = {Englmeier, Jana and von Hoermann, Christian and Rieker, Daniel and Benbow, Marc Eric and Benjamin, Caryl and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Lackner, Tom{\´a}š and Mitesser, Oliver and Redlich, Sarah and Riebl, Rebekka and Rojas-Botero, Sandra and Rummler, Thomas and Salamon, J{\"o}rg-Alfred and Sommer, David and Steffan-Dewenter, Ingolf and Tobisch, Cynthia and Uhler, Johannes and Uphus, Lars and Zhang, Jie and M{\"u}ller, J{\"o}rg}, title = {Dung-visiting beetle diversity is mainly affected by land use, while community specialization is driven by climate}, series = {Ecology and Evolution}, volume = {12}, journal = {Ecology and Evolution}, number = {10}, issn = {2045-7758}, doi = {10.1002/ece3.9386}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-312846}, year = {2022}, abstract = {Dung beetles are important actors in the self-regulation of ecosystems by driving nutrient cycling, bioturbation, and pest suppression. Urbanization and the sprawl of agricultural areas, however, destroy natural habitats and may threaten dung beetle diversity. In addition, climate change may cause shifts in geographical distribution and community composition. We used a space-for-time approach to test the effects of land use and climate on α-diversity, local community specialization (H\(_2\)′) on dung resources, and γ-diversity of dung-visiting beetles. For this, we used pitfall traps baited with four different dung types at 115 study sites, distributed over a spatial extent of 300 km × 300 km and 1000 m in elevation. Study sites were established in four local land-use types: forests, grasslands, arable sites, and settlements, embedded in near-natural, agricultural, or urban landscapes. Our results show that abundance and species density of dung-visiting beetles were negatively affected by agricultural land use at both spatial scales, whereas γ-diversity at the local scale was negatively affected by settlements and on a landscape scale equally by agricultural and urban land use. Increasing precipitation diminished dung-visiting beetle abundance, and higher temperatures reduced community specialization on dung types and γ-diversity. These results indicate that intensive land use and high temperatures may cause a loss in dung-visiting beetle diversity and alter community networks. A decrease in dung-visiting beetle diversity may disturb decomposition processes at both local and landscape scales and alter ecosystem functioning, which may lead to drastic ecological and economic damage.}, language = {en} } @article{RedlichZhangBenjaminetal.2022, author = {Redlich, Sarah and Zhang, Jie and Benjamin, Caryl and Dhillon, Maninder Singh and Englmeier, Jana and Ewald, J{\"o}rg and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Hovestadt, Thomas and Kollmann, Johannes and Koellner, Thomas and K{\"u}bert-Flock, Carina and Kunstmann, Harald and Menzel, Annette and Moning, Christoph and Peters, Wibke and Riebl, Rebekka and Rummler, Thomas and Rojas-Botero, Sandra and Tobisch, Cynthia and Uhler, Johannes and Uphus, Lars and M{\"u}ller, J{\"o}rg and Steffan-Dewenter, Ingolf}, title = {Disentangling effects of climate and land use on biodiversity and ecosystem services—A multi-scale experimental design}, series = {Methods in Ecology and Evolution}, volume = {13}, journal = {Methods in Ecology and Evolution}, number = {2}, doi = {10.1111/2041-210X.13759}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258270}, pages = {514-527}, year = {2022}, abstract = {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.}, language = {en} } @article{UhlerRedlichZhangetal.2021, author = {Uhler, Johannes and Redlich, Sarah and Zhang, Jie and Hothorn, Torsten and Tobisch, Cynthia and Ewald, J{\"o}rg and Thorn, Simon and Seibold, Sebastian and Mitesser, Oliver and Morin{\`e}re, J{\´e}r{\^o}me and Bozicevic, Vedran and Benjamin, Caryl S. and Englmeier, Jana and Fricke, Ute and Ganuza, Cristina and Haensel, Maria and Riebl, Rebekka and Rojas-Botero, Sandra and Rummler, Thomas and Uphus, Lars and Schmidt, Stefan and Steffan-Dewenter, Ingolf and M{\"u}ller, J{\"o}rg}, title = {Relationships of insect biomass and richness with land use along a climate gradient}, series = {Nature Communications}, volume = {12}, journal = {Nature Communications}, number = {1}, doi = {10.1038/s41467-021-26181-3}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-265058}, year = {2021}, abstract = {Recently reported insect declines have raised both political and social concern. Although the declines have been attributed to land use and climate change, supporting evidence suffers from low taxonomic resolution, short time series, a focus on local scales, and the collinearity of the identified drivers. In this study, we conducted a systematic assessment of insect populations in southern Germany, which showed that differences in insect biomass and richness are highly context dependent. We found the largest difference in biomass between semi-natural and urban environments (-42\%), whereas differences in total richness (-29\%) and the richness of threatened species (-56\%) were largest from semi-natural to agricultural environments. These results point to urbanization and agriculture as major drivers of decline. We also found that richness and biomass increase monotonously with increasing temperature, independent of habitat. The contrasting patterns of insect biomass and richness question the use of these indicators as mutual surrogates. Our study provides support for the implementation of more comprehensive measures aimed at habitat restoration in order to halt insect declines.}, language = {en} }