526 Mathematische Geografie
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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.
Ouagadougou and Bobo-Dioulasso remain the two major urban centers in Burkina Faso with an increasing trend in human footprint. The research aimed at analyzing the Land Use/Land Cover (LULC) dynamics in the two cities between 2003 and 2021 using intensity analysis, which decomposes LULC changes into interval, category and transition levels. The satellite data used for this research were composed of surface reflectance imagery from Landsat 5, Landsat 7 and Landsat 8 acquired from the Google Earth Engine Data Catalogue. The Random Forest, Support Vector Machine and Gradient Tree Boost algorithms were employed to run supervised image classifications for four selected years including 2003, 2009, 2015 and 2021. The results showed that the landscape is changing in both cities due to rapid urbanization. Ouagadougou experienced more rapid changes than Bobo-Dioulasso, with a maximum annual change intensity of 3.61% recorded between 2015 and 2021 against 2.22% in Bobo-Dioulasso for the period 2009–2015. The transition of change was mainly towards built-up areas, which gain targeted bare and agricultural lands in both cities. This situation has led to a 78.12% increase of built-up surfaces in Ouagadougou, while 42.24% of agricultural land area was lost. However, in Bobo-Dioulasso, the built class has increased far more by 140.67%, and the agricultural land areas experienced a gain of 1.38% compared with the 2003 baseline. The study demonstrates that the human footprint is increasing in both cities making the inhabitants vulnerable to environmental threats such as flooding and the effect of an Urban Heat Island, which is information that could serve as guide for sustainable urban land use planning.
The occurrence of a likely graptolite in lowest Wuliuan strata of the Franconian Forest almost certainly records the oldest known graptolithoid hemichordate in West Gondwana and possibly the oldest graptolite presently known. The fossil is a delicate, erect, apparently unbranched rhabdosome with narrow thecae tentatively assigned to the poorly known genus Ovetograptus of the Dithecodendridae. This report includes an overview of pre-Furongian graptolithoids with slight corrections on the stratigraphic position of earlier reported species.
Performance assessment of CORDEX regional climate models in wind speed simulations over Zambia
(2023)
There is no single solution to cutting emissions, however, renewable energy projects that are backed by rigorous ex-ante assessments play an important role in these efforts. An inspection of literature reveals critical knowledge gaps in the understanding of future wind speed variability across Zambia, thus leading to major uncertainties in the understanding of renewable wind energy potential over the country. Several model performance metrics, both statistical and graphical were used in this study to examine the performance of CORDEX Africa Regional Climate Models (RCMs) in simulating wind speed across Zambia. Results indicate that wind speed is increasing at the rate of 0.006 m s\(^{−1}\) per year. RCA4-GFDL-ESM2M, RCA4-HadGEM2-ES, RCA4-IPSL-CM5A-MR, and RCA4-CSIRO-MK3.6.0 were found to correctly simulate wind speed increase with varying magnitudes on the Sen’s estimator of slope. All the models sufficiently reproduce the annual cycle of wind speed with a steady increase being observed from April reaching its peak around August/September and beginning to drop in October. Apart from RegCM4-MPI-ESM and RegCM4-HadGEM2, the performance of RCMs in simulating spatial wind speed patterns is generally good although they overestimate it by ~ 1 m s\(^{−1}\) in the western and southern provinces of the country. Model performance metrics indicate that with a correlation coefficient of 0.5, a root mean square error of 0.4 m s\(^{−1}\), an RSR value of 7.7 and a bias of 19.9%, RCA4-GFDL-ESM2M outperforms all other models followed by RCA4-HadGEM2, and RCA4-CM5A-MR respectively. These results, therefore, suggest that studies that use an ensemble of RCA4-GFDL-ESM2M, RCA4-HadGEM2, and RCA4-CM5A-MR would yield useful results for informing future renewable wind energy potential in Zambia.
Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d’Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user’s accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d’Ivoire.
The positive phase of the subtropical Indian Ocean dipole (SIOD) is one of the climatic modes in the subtropical southern Indian Ocean that influences the austral summer inter-annual rainfall variability in parts of southern Africa. This paper examines austral summer rain-bearing circulation types (CTs) in Africa south of the equator that are related to the positive SIOD and the dynamics through which specific rainfall regions in southern Africa can be influenced by this relationship. Four austral summer rain-bearing CTs were obtained. Among the four CTs, the CT that featured (i) enhanced cyclonic activity in the southwest Indian Ocean; (ii) positive widespread rainfall anomaly in the southwest Indian Ocean; and (iii) low-level convergence of moisture fluxes from the tropical South Atlantic Ocean, tropical Indian Ocean, and the southwest Indian Ocean, over the south-central landmass of Africa, was found to be related to the positive SIOD climatic mode. The relationship also implies that positive SIOD can be expected to increase the amplitude and frequency of occurrence of the aforementioned CT. The linkage between the CT related to the positive SIOD and austral summer homogeneous regions of rainfall anomalies in Africa south of the equator showed that it is the principal CT that is related to the inter-annual rainfall variability of the south-central regions of Africa, where the SIOD is already known to significantly influence its rainfall variability. Hence, through the large-scale patterns of atmospheric circulation associated with the CT, the SIOD can influence the spatial distribution and intensity of rainfall over the preferred landmass through enhanced moisture convergence.
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.
A circum-Arctic monitoring framework for quantifying annual erosion rates of permafrost coasts
(2023)
This study demonstrates a circum-Arctic monitoring framework for quantifying annual change of permafrost-affected coasts at a spatial resolution of 10 m. Frequent cloud coverage and challenging lighting conditions, including polar night, limit the usability of optical data in Arctic regions. For this reason, Synthetic Aperture RADAR (SAR) data in the form of annual median and standard deviation (sd) Sentinel-1 (S1) backscatter images covering the months June–September for the years 2017–2021 were computed. Annual composites for the year 2020 were hereby utilized as input for the generation of a high-quality coastline product via a Deep Learning (DL) workflow, covering 161,600 km of the Arctic coastline. The previously computed annual S1 composites for the years 2017 and 2021 were employed as input data for the Change Vector Analysis (CVA)-based coastal change investigation. The generated DL coastline product served hereby as a reference. Maximum erosion rates of up to 67 m per year could be observed based on 400 m coastline segments. Overall highest average annual erosion can be reported for the United States (Alaska) with 0.75 m per year, followed by Russia with 0.62 m per year. Out of all seas covered in this study, the Beaufort Sea featured the overall strongest average annual coastal erosion of 1.12 m. Several quality layers are provided for both the DL coastline product and the CVA-based coastal change analysis to assess the applicability and accuracy of the output products. The predicted coastal change rates show good agreement with findings published in previous literature. The proposed methods and data may act as a valuable tool for future analysis of permafrost loss and carbon emissions in Arctic coastal environments.
Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience.
On a daily basis, political decisions are made, often with their full extent of impact being unclear. Not seldom, the decisions and policy measures implemented result in direct or indirect unintended negative impacts, such as on the natural environment, which can vary in time, space, nature, and severity. To achieve a more sustainable world with equitable societies requires fundamental rethinking of our policymaking. It calls for informed decision making and a monitoring of political impact for which evidence-based knowledge is necessary. The most powerful tool to derive objective and systematic spatial information and, thus, add to transparent decisions is remote sensing (RS). This review analyses how spaceborne RS is used by the scientific community to provide evidence for the policymaking process. We reviewed 194 scientific publications from 2015 to 2020 and analysed them based on general insights (e.g., study area) and RS application-related information (e.g., RS data and products). Further, we classified the studies according to their degree of science–policy integration by determining their engagement with the political field and their potential contribution towards four stages of the policy cycle: problem identification/knowledge building, policy formulation, policy implementation, and policy monitoring and evaluation. Except for four studies, we found that studies had not directly involved or informed the policy field or policymaking process. Most studies contributed to the stage problem identification/knowledge building, followed by ex post policy impact assessment. To strengthen the use of RS for policy-relevant studies, the concept of the policy cycle is used to showcase opportunities of RS application for the policymaking process. Topics gaining importance and future requirements of RS at the science–policy interface are identified. If tackled, RS can be a powerful complement to provide policy-relevant evidence to shed light on the impact of political decisions and thus help promote sustainable development from the core.