@article{MahmoudDukerConradetal.2016, author = {Mahmoud, Mahmoud Ibrahim and Duker, Alfred and Conrad, Christopher and Thiel, Michael and Ahmad, Halilu Shaba}, title = {Analysis of Settlement Expansion and Urban Growth Modelling Using Geoinformation for Assessing Potential Impacts of Urbanization on Climate in Abuja City, Nigeria}, series = {Remote Sensing}, volume = {8}, journal = {Remote Sensing}, number = {3}, doi = {10.3390/rs8030220}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146644}, pages = {220}, year = {2016}, abstract = {This study analyzed the spatiotemporal pattern of settlement expansion in Abuja, Nigeria, one of West Africa's fastest developing cities, using geoinformation and ancillary datasets. Three epochs of Land-use Land-cover (LULC) maps for 1986, 2001 and 2014 were derived from Landsat images using support vector machines (SVM). Accuracy assessment (AA) of the LULC maps based on the pixel count resulted in overall accuracy of 82\%, 92\% and 92\%, while the AA derived from the error adjusted area (EAA) method stood at 69\%, 91\% and 91\% for 1986, 2001 and 2014, respectively. Two major techniques for detecting changes in the LULC epochs involved the use of binary maps as well as a post-classification comparison approach. Quantitative spatiotemporal analysis was conducted to detect LULC changes with specific focus on the settlement development pattern of Abuja, the federal capital city (FCC) of Nigeria. Logical transitions to the urban category were modelled for predicting future scenarios for the year 2050 using the embedded land change modeler (LCM) in the IDRISI package. Based on the EAA, the result showed that urban areas increased by more than 11\% between 1986 and 2001. In contrast, this value rose to 17\% between 2001 and 2014. The LCM model projected LULC changes that showed a growing trend in settlement expansion, which might take over allotted spaces for green areas and agricultural land if stringent development policies and enforcement measures are not implemented. In conclusion, integrating geospatial technologies with ancillary datasets offered improved understanding of how urbanization processes such as increased imperviousness of such a magnitude could influence the urban microclimate through the alteration of natural land surface temperature. Urban expansion could also lead to increased surface runoff as well as changes in drainage geography leading to urban floods.}, language = {en} } @article{ZoungranaConradAmekudzietal.2015, author = {Zoungrana, Benewinde Jean-Bosco and Conrad, Christopher and Amekudzi, Leonard K. and Thiel, Michael and Dapola Da, Evariste and Forkuor, Gerald and L{\"o}w, Fabian}, title = {Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa}, series = {Remote Sensing}, volume = {7}, journal = {Remote Sensing}, number = {9}, doi = {10.3390/rs70912076}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-125866}, pages = {12076-12102}, year = {2015}, abstract = {Accurate quantification of land use/cover change (LULCC) is important for efficient environmental management, especially in regions that are extremely affected by climate variability and continuous population growth such as West Africa. In this context, accurate LULC classification and statistically sound change area estimates are essential for a better understanding of LULCC processes. This study aimed at comparing mono-temporal and multi-temporal LULC classifications as well as their combination with ancillary data and to determine LULCC across the heterogeneous landscape of southwest Burkina Faso using accurate classification results. Landsat data (1999, 2006 and 2011) and ancillary data served as input features for the random forest classifier algorithm. Five LULC classes were identified: woodland, mixed vegetation, bare surface, water and agricultural area. A reference database was established using different sources including high-resolution images, aerial photo and field data. LULCC and LULC classification accuracies, area and area uncertainty were computed based on the method of adjusted error matrices. The results revealed that multi-temporal classification significantly outperformed those solely based on mono-temporal data in the study area. However, combining mono-temporal imagery and ancillary data for LULC classification had the same accuracy level as multi-temporal classification which is an indication that this combination is an efficient alternative to multi-temporal classification in the study region, where cloud free images are rare. The LULCC map obtained had an overall accuracy of 92\%. Natural vegetation loss was estimated to be 17.9\% ± 2.5\% between 1999 and 2011. The study area experienced an increase in agricultural area and bare surface at the expense of woodland and mixed vegetation, which attests to the ongoing deforestation. These results can serve as means of regional and global land cover products validation, as they provide a new validated data set with uncertainty estimates in heterogeneous ecosystems prone to classification errors.}, language = {en} } @article{ForkuorConradThieletal.2014, author = {Forkuor, Gerald and Conrad, Christopher and Thiel, Michael and Ullmann, Tobias and Zoungrana, Evence}, title = {Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa}, doi = {10.3390/rs6076472}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-113070}, year = {2014}, abstract = {Crop mapping in West Africa is challenging, due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. To address this challenge, we integrated high spatial resolution multi-temporal optical (RapidEye) and dual polarized (VV/VH) SAR (TerraSAR-X) data to map crops and crop groups in northwestern Benin using the random forest classification algorithm. The overall goal was to ascertain the contribution of the SAR data to crop mapping in the region. A per-pixel classification result was overlaid with vector field boundaries derived from image segmentation, and a crop type was determined for each field based on the modal class within the field. A per-field accuracy assessment was conducted by comparing the final classification result with reference data derived from a field campaign. Results indicate that the integration of RapidEye and TerraSAR-X data improved classification accuracy by 10\%-15\% over the use of RapidEye only. The VV polarization was found to better discriminate crop types than the VH polarization. The research has shown that if optical and SAR data are available for the whole cropping season, classification accuracies of up to 75\% are achievable.}, language = {en} } @article{NyamekyeThielSchoenbrodtStittetal.2018, author = {Nyamekye, Clement and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah and Zoungrana, Benewinde J.-B. and Amekudzi, Leonard K.}, title = {Soil and water conservation in Burkina Faso, West Africa}, series = {Sustainability}, volume = {10}, journal = {Sustainability}, number = {9}, issn = {2071-1050}, doi = {10.3390/su10093182}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-197653}, pages = {3182}, year = {2018}, abstract = {Inadequate land management and agricultural activities have largely resulted in land degradation in Burkina Faso. The nationwide governmental and institutional driven implementation and adoption of soil and water conservation measures (SWCM) since the early 1960s, however, is expected to successively slow down the degradation process and to increase the agricultural output. Even though relevant measures have been taken, only a few studies have been conducted to quantify their effect, for instance, on soil erosion and environmental restoration. In addition, a comprehensive summary of initiatives, implementation strategies, and eventually region-specific requirements for adopting different SWCM is missing. The present study therefore aims to review the different SWCM in Burkina Faso and implementation programs, as well as to provide information on their effects on environmental restoration and agricultural productivity. This was achieved by considering over 143 studies focusing on Burkina Faso's experience and research progress in areas of SWCM and soil erosion. SWCM in Burkina Faso have largely resulted in an increase in agricultural productivity and improvement in food security. Finally, this study aims at supporting the country's informed decision-making for extending already existing SWCM and for deriving further implementation strategies.}, language = {en} } @article{ForkuorHounkpatinWelpetal.2017, author = {Forkuor, Gerald and Hounkpatin, Ozias K.L. and Welp, Gerhard and Thiel, Michael}, title = {High resolution mapping of soil properties using remote sensing variables in south-western Burkina Faso: a comparison of machine learning and multiple linear regression models}, series = {PLOS One}, volume = {12}, journal = {PLOS One}, number = {1}, doi = {10.1371/journal.pone.0170478}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-180978}, pages = {21}, year = {2017}, abstract = {Accurate and detailed spatial soil information is essential for environmental modelling, risk assessment and decision making. The use of Remote Sensing data as secondary sources of information in digital soil mapping has been found to be cost effective and less time consuming compared to traditional soil mapping approaches. But the potentials of Remote Sensing data in improving knowledge of local scale soil information in West Africa have not been fully explored. This study investigated the use of high spatial resolution satellite data (RapidEye and Landsat), terrain/climatic data and laboratory analysed soil samples to map the spatial distribution of six soil properties-sand, silt, clay, cation exchange capacity (CEC), soil organic carbon (SOC) and nitrogen-in a 580 km2 agricultural watershed in south-western Burkina Faso. Four statistical prediction models-multiple linear regression (MLR), random forest regression (RFR), support vector machine (SVM), stochastic gradient boosting (SGB)-were tested and compared. Internal validation was conducted by cross validation while the predictions were validated against an independent set of soil samples considering the modelling area and an extrapolation area. Model performance statistics revealed that the machine learning techniques performed marginally better than the MLR, with the RFR providing in most cases the highest accuracy. The inability of MLR to handle non-linear relationships between dependent and independent variables was found to be a limitation in accurately predicting soil properties at unsampled locations. Satellite data acquired during ploughing or early crop development stages (e.g. May, June) were found to be the most important spectral predictors while elevation, temperature and precipitation came up as prominent terrain/climatic variables in predicting soil properties. The results further showed that shortwave infrared and near infrared channels of Landsat8 as well as soil specific indices of redness, coloration and saturation were prominent predictors in digital soil mapping. Considering the increased availability of freely available Remote Sensing data (e.g. Landsat, SRTM, Sentinels), soil information at local and regional scales in data poor regions such as West Africa can be improved with relatively little financial and human resources.}, language = {en} } @article{KleemannZamoraVillacisChiluisaetal.2022, author = {Kleemann, Janina and Zamora, Camilo and Villacis-Chiluisa, Alexandra Belen and Cuenca, Pablo and Koo, Hongmi and Noh, Jin Kyoung and F{\"u}rst, Christine and Thiel, Michael}, title = {Deforestation in continental Ecuador with a focus on protected areas}, series = {Land}, volume = {11}, journal = {Land}, number = {2}, issn = {2073-445X}, doi = {10.3390/land11020268}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-262078}, year = {2022}, abstract = {Forest conservation is of particular concern in tropical regions where a large refuge of biodiversity is still existing. These areas are threatened by deforestation, forest degradation and fragmentation. Especially, pressures of anthropogenic activities adjacent to these areas significantly influence conservation effectiveness. Ecuador was chosen as study area since it is a globally relevant center of forest ecosystems and biodiversity. We identified hotspots of deforestation on the national level of continental Ecuador between 1990 and 2018, analyzed the most significant drivers of deforestation on national and biome level (the Coast, the Andes, The Amazon) as well as inside protected areas in Ecuador by using multiple regression analysis. We separated the national system of protected areas (SNAP) into higher and lower protection levels. Besides SNAP, we also considered Biosphere Reserves (BRs) and Ramsar sites. In addition, we investigated the rates and spatial patterns of deforestation in protected areas and buffer zones (5 km and 10 km outwards the protected area boundaries) using landscape metrics. Between 1990 and 2018, approximately 4\% of the accumulated deforestation occurred within the boundaries of SNAP, and up to 25.5\% in buffer zones. The highest rates of deforestation have been found in the 5 km buffer zone around the protected areas with the highest protection level. Protected areas and their buffer zones with higher protection status were identified as the most deforested areas among SNAP. BRs had the highest deforestation rates among all protected areas but most of these areas just became BRs after the year 2000. The most important driver of deforestation is agriculture. Other relevant drivers differ between the biomes. The results suggest that the SNAP is generally effective to prevent deforestation within their protection boundaries. However, deforestation around protected areas can undermine conservation strategies to sustain biodiversity. Actions to address such dynamics and patterns of deforestation and forest fragmentation, and developing conservation strategies of their landscape context are urgently needed especially in the buffer zones of areas with the highest protection status.}, language = {en} } @article{RichardAbdelRahmanSubramanianetal.2017, author = {Richard, Kyalo and Abdel-Rahman, Elfatih M. and Subramanian, Sevgan and Nyasani, Johnson O. and Thiel, Michael and Jozani, Hosein and Borgemeister, Christian and Landmann, Tobias}, title = {Maize cropping systems mapping using RapidEye observations in agro-ecological landscapes in Kenya}, series = {Sensors}, volume = {17}, journal = {Sensors}, number = {11}, doi = {10.3390/s17112537}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-173285}, year = {2017}, abstract = {Cropping systems information on explicit scales is an important but rarely available variable in many crops modeling routines and of utmost importance for understanding pests and disease propagation mechanisms in agro-ecological landscapes. In this study, high spatial and temporal resolution RapidEye bio-temporal data were utilized within a novel 2-step hierarchical random forest (RF) classification approach to map areas of mono- and mixed maize cropping systems. A small-scale maize farming site in Machakos County, Kenya was used as a study site. Within the study site, field data was collected during the satellite acquisition period on general land use/land cover (LULC) and the two cropping systems. Firstly, non-cropland areas were masked out from other land use/land cover using the LULC mapping result. Subsequently an optimized RF model was applied to the cropland layer to map the two cropping systems (2nd classification step). An overall accuracy of 93\% was attained for the LULC classification, while the class accuracies (PA: producer's accuracy and UA: user's accuracy) for the two cropping systems were consistently above 85\%. We concluded that explicit mapping of different cropping systems is feasible in complex and highly fragmented agro-ecological landscapes if high resolution and multi-temporal satellite data such as 5 m RapidEye data is employed. Further research is needed on the feasibility of using freely available 10-20 m Sentinel-2 data for wide-area assessment of cropping systems as an important variable in numerous crop productivity models.}, language = {en} } @article{RieserVesteThieletal.2021, author = {Rieser, Jakob and Veste, Maik and Thiel, Michael and Sch{\"o}nbrodt-Stitt, Sarah}, title = {Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland}, series = {Remote Sensing}, volume = {13}, journal = {Remote Sensing}, number = {16}, issn = {2072-4292}, doi = {10.3390/rs13163093}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-245006}, year = {2021}, abstract = {Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs' activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the 'Lieberoser Heide' in northeastern Germany, is home to the country's largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9\%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.}, language = {en} } @article{MeyerPetersThieletal.2021, author = {Meyer, Constantin and Peters, Jan Christoph and Thiel, Michael and Rathmann, Joachim and Job, Hubert}, title = {Monitoring von Freifl{\"a}cheninanspruchnahme und -versiegelung f{\"u}r eine nachhaltige Raumentwicklung in Bayern}, series = {Raumforschung und Raumordnung}, volume = {79}, journal = {Raumforschung und Raumordnung}, number = {2}, doi = {10.14512/rur.40}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261622}, pages = {172-189}, year = {2021}, abstract = {Im Freistaat Bayern wird intensiv diskutiert, wie die nach wie vor hohe Freifl{\"a}cheninanspruchnahme f{\"u}r Siedlungs- und Verkehrszwecke reduziert werden kann. Wissenschaftliche Grundlage f{\"u}r Steuerungsans{\"a}tze in der Stadt- und Regionalentwicklung sollte ein verbessertes staatliches Fl{\"a}chenmonitoring sein, welches {\"u}ber die amtliche Statistik und deren Hauptindikator "Siedlungs- und Verkehrsfl{\"a}che" hinaus auch die qualitative Dimension der Fl{\"a}cheninanspruchnahme einbezieht. Daf{\"u}r stellt dieser Beitrag methodische Erweiterungsans{\"a}tze f{\"u}r das Fl{\"a}chenmonitoring vor, welche kleinr{\"a}umige Analysen der Zersiedelung, Freiraumstruktur, Fl{\"a}chenversiegelung und {\"O}kosystemleistungen am Beispiel des Landkreises Rh{\"o}n-Grabfeld aufzeigen. Diese werden im Kontext der Debatte zu Ursachen und Steuerung der Freifl{\"a}cheninanspruchnahme sowie zu aktuellen Anforderungen an das Fl{\"a}chenmonitoring diskutiert. Betont wird deren Bedeutung f{\"u}r das Monitoring rechtlicher Vorgaben und politischer Ziele zur nachhaltigen Fl{\"a}chennutzung.}, language = {de} } @article{OuedraogoHackmanThieletal.2023, author = {Ouedraogo, Valentin and Hackman, Kwame Oppong and Thiel, Michael and Dukiya, Jaiye}, title = {Intensity analysis for urban Land Use/Land Cover dynamics characterization of Ouagadougou and Bobo-Dioulasso in Burkina Faso}, series = {Land}, volume = {12}, journal = {Land}, number = {5}, issn = {2073-445X}, doi = {10.3390/land12051063}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-319397}, year = {2023}, abstract = {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.}, language = {en} } @article{AnsahAbuKleemannetal.2022, author = {Ansah, Christabel Edena and Abu, Itohan-Osa and Kleemann, Janina and Mahmoud, Mahmoud Ibrahim and Thiel, Michael}, title = {Environmental contamination of a biodiversity hotspot — action needed for nature conservation in the Niger Delta, Nigeria}, series = {Sustainability}, volume = {14}, journal = {Sustainability}, number = {21}, issn = {2071-1050}, doi = {10.3390/su142114256}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-297214}, year = {2022}, abstract = {The Niger Delta belongs to the largest swamp and mangrove forests in the world hosting many endemic and endangered species. Therefore, its conservation should be of highest priority. However, the Niger Delta is confronted with overexploitation, deforestation and pollution to a large extent. In particular, oil spills threaten the biodiversity, ecosystem services, and local people. Remote sensing can support the detection of spills and their potential impact when accessibility on site is difficult. We tested different vegetation indices to assess the impact of oil spills on the land cover as well as to detect accumulations (hotspots) of oil spills. We further identified which species, land cover types, and protected areas could be threatened in the Niger Delta due to oil spills. The results showed that the Enhanced Vegetation Index, the Normalized Difference Vegetation Index, and the Soil Adjusted Vegetation Index were more sensitive to the effects of oil spills on different vegetation cover than other tested vegetation indices. Forest cover was the most affected land-cover type and oil spills also occurred in protected areas. Threatened species are inhabiting the Niger Delta Swamp Forest and the Central African Mangroves that were mainly affected by oil spills and, therefore, strong conservation measures are needed even though security issues hamper the monitoring and control.}, language = {en} } @article{KanmegneTamgaLatifiUllmannetal.2023, author = {Kanmegne Tamga, Dan and Latifi, Hooman and Ullmann, Tobias and Baumhauer, Roland and Thiel, Michael and Bayala, Jules}, title = {Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems}, series = {Agroforestry Systems}, volume = {97}, journal = {Agroforestry Systems}, number = {1}, issn = {0167-4366}, doi = {10.1007/s10457-022-00791-2}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-324139}, pages = {109-119}, year = {2023}, abstract = {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 C{\^o}te d'Ivoire, close to the Ta{\"i} 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 C{\^o}te d'Ivoire.}, language = {en} } @article{BellKleinRieseretal.2023, author = {Bell, Alexandra and Klein, Doris and Rieser, Jakob and Kraus, Tanja and Thiel, Michael and Dech, Stefan}, title = {Scientific evidence from space — a review of spaceborne remote sensing applications at the science-policy interface}, series = {Remote Sensing}, volume = {15}, journal = {Remote Sensing}, number = {4}, issn = {2072-4292}, doi = {10.3390/rs15040940}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-303925}, year = {2023}, abstract = {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.}, language = {en} }