TY - JOUR A1 - Forkuor, Gerald A1 - Ullmann, Tobias A1 - Griesbeck, Mario T1 - Mapping and monitoring small-scale mining activities in Ghana using Sentinel-1 time series (2015−2019) JF - Remote Sensing N2 - Illegal small-scale mining (galamsey) in South-Western Ghana has grown tremendously in the last decade and caused significant environmental degradation. Excessive cloud cover in the area has limited the use of optical remote sensing data to map and monitor the extent of these activities. This study investigated the use of annual time-series Sentinel-1 data to map and monitor illegal mining activities along major rivers in South-Western Ghana between 2015 and 2019. A change detection approach, based on three time-series features — minimum, mean, maximum — was used to compute a backscatter threshold value suitable to identify/detect mining-induced land cover changes in the study area. Compared to the mean and maximum, the minimum time-series feature (in both VH and VV polarization) was found to be more sensitive to changes in backscattering within the period of investigation. Our approach permitted the detection of new illegal mining areas on an annual basis. A backscatter threshold value of +1.65 dB was found suitable for detecting illegal mining activities in the study area. Application of this threshold revealed illegal mining area extents of 102 km\(^2\), 60 km\(^2\) and 33 km\(^2\) for periods 2015/2016–2016/2017, 2016/2017–2017/2018 and 2017/2018–2018/2019, respectively. The observed decreasing trend in new illegal mining areas suggests that efforts at stopping illegal mining yielded positive results in the period investigated. Despite the advantages of Synthetic Aperture Radar data in monitoring phenomena in cloud-prone areas, our analysis revealed that about 25% of the Sentinel-1 data, mostly acquired in March and October (beginning and end of rainy season respectively), were unusable due to atmospheric effects from high intensity rainfall events. Further investigation in other geographies and climatic regions is needed to ascertain the susceptibility of Sentinel-1 data to atmospheric conditions. KW - Sentine-1 KW - mining KW - image artifacts KW - time-series features KW - galamsey KW - Ghana Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-203204 SN - 2072-4292 VL - 12 IS - 6 ER - TY - JOUR A1 - Knauer, Kim A1 - Gessner, Ursula A1 - Fensholt, Rasmus A1 - Forkuor, Gerald A1 - Kuenzer, Claudia T1 - Monitoring agricultural expansion in Burkina Faso over 14 years with 30 m resolution time series: the role of population growth and implications for the environment JF - Remote Sensing N2 - Burkina Faso ranges amongst the fastest growing countries in the world with an annual population growth rate of more than three percent. This trend has consequences for food security since agricultural productivity is still on a comparatively low level in Burkina Faso. In order to compensate for the low productivity, the agricultural areas are expanding quickly. The mapping and monitoring of this expansion is difficult, even on the basis of remote sensing imagery, since the extensive farming practices and frequent cloud coverage in the area make the delineation of cultivated land from other land cover and land use types a challenging task. However, as the rapidly increasing population could have considerable effects on the natural resources and on the regional development of the country, methods for improved mapping of LULCC (land use and land cover change) are needed. For this study, we applied the newly developed ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) framework to generate high temporal (8-day) and high spatial (30 m) resolution NDVI time series for all of Burkina Faso for the years 2001, 2007, and 2014. For this purpose, more than 500 Landsat scenes and 3000 MODIS scenes were processed with this automated framework. The generated ESTARFM NDVI time series enabled extraction of per-pixel phenological features that all together served as input for the delineation of agricultural areas via random forest classification at 30 m spatial resolution for entire Burkina Faso and the three years. For training and validation, a randomly sampled reference dataset was generated from Google Earth images and based on expert knowledge. The overall accuracies of 92% (2001), 91% (2007), and 91% (2014) indicate the well-functioning of the applied methodology. The results show an expansion of agricultural area of 91% between 2001 and 2014 to a total of 116,900 km\(^2\). While rainfed agricultural areas account for the major part of this trend, irrigated areas and plantations also increased considerably, primarily promoted by specific development projects. This expansion goes in line with the rapid population growth in most provinces of Burkina Faso where land was still available for an expansion of agricultural area. The analysis of agricultural encroachment into protected areas and their surroundings highlights the increased human pressure on these areas and the challenges of environmental protection for the future. KW - remote sensing KW - Africa KW - agriculture KW - Burkina Faso KW - data fusion KW - ESTARFM framework KW - irrigation KW - land surface phenology KW - Landsat KW - MODIS KW - plantation KW - protected areas KW - TIMESAT Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-171905 VL - 9 IS - 2 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Hounkpatin, Ozias K.L. A1 - Welp, Gerhard A1 - Thiel, Michael T1 - 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 JF - PLOS One N2 - 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. KW - Agricultural soil science KW - Forecasting KW - Machine learning KW - Support vector machines KW - Paleopedology KW - Trees KW - Clay mineralogy KW - Remote sensing KW - South-western Burkina Faso Y1 - 2017 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-180978 VL - 12 IS - 1 ER - TY - JOUR A1 - Zoungrana, Benewinde Jean-Bosco A1 - Conrad, Christopher A1 - Amekudzi, Leonard K. A1 - Thiel, Michael A1 - Dapola Da, Evariste A1 - Forkuor, Gerald A1 - Löw, Fabian T1 - Multi-Temporal Landsat Images and Ancillary Data for Land Use/Cover Change (LULCC) Detection in the Southwest of Burkina Faso, West Africa JF - Remote Sensing N2 - 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. KW - Burkina Faso KW - West Africa KW - multi-temporal images KW - mono-temporal image KW - ancillary data KW - LULCC Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-125866 VL - 7 IS - 9 ER - TY - JOUR A1 - Asare-Kyei, Daniel A1 - Forkuor, Gerald A1 - Venus, Valentijn T1 - Modeling Flood Hazard Zones at the Sub-District Level with the Rational Model Integrated with GIS and Remote Sensing Approaches JF - Water N2 - Robust risk assessment requires accurate flood intensity area mapping to allow for the identification of populations and elements at risk. However, available flood maps in West Africa lack spatial variability while global datasets have resolutions too coarse to be relevant for local scale risk assessment. Consequently, local disaster managers are forced to use traditional methods such as watermarks on buildings and media reports to identify flood hazard areas. In this study, remote sensing and Geographic Information System (GIS) techniques were combined with hydrological and statistical models to delineate the spatial limits of flood hazard zones in selected communities in Ghana, Burkina Faso and Benin. The approach involves estimating peak runoff concentrations at different elevations and then applying statistical methods to develop a Flood Hazard Index (FHI). Results show that about half of the study areas fall into high intensity flood zones. Empirical validation using statistical confusion matrix and the principles of Participatory GIS show that flood hazard areas could be mapped at an accuracy ranging from 77% to 81%. This was supported with local expert knowledge which accurately classified 79% of communities deemed to be highly susceptible to flood hazard. The results will assist disaster managers to reduce the risk to flood disasters at the community level where risk outcomes are first materialized. KW - climate change KW - rational model KW - community KW - flood hazard index KW - West Africa KW - GIS KW - vulnerability KW - performance KW - impact KW - risk KW - mapping KW - runoff Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-151581 VL - 7 SP - 3531 EP - 3564 ER - TY - JOUR A1 - Forkuor, Gerald A1 - Conrad, Christopher A1 - Thiel, Michael A1 - Ullmann, Tobias A1 - Zoungrana, Evence T1 - Integration of Optical and Synthetic Aperture Radar Imagery for Improving Crop Mapping in Northwestern Benin, West Africa N2 - 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. KW - random forest KW - crop mapping KW - agriculture KW - West Africa KW - RapidEye KW - TerraSAR-X Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-113070 ER - TY - THES A1 - Forkuor, Gerald T1 - Agricultural Land Use Mapping in West Africa Using Multi-sensor Satellite Imagery T1 - Kartierung landwirtschaftlicher Landnutzung unter Verwendung multi-sensoraler Satellitendaten N2 - Rapid population growth in West Africa has led to expansion in croplands due to the need to grow more food to meet the rising food demand of the burgeoning population. These expansions negatively impact the sub-region's ecosystem, with implications for water and soil quality, biodiversity and climate. In order to appropriately monitor the changes in croplands and assess its impact on the ecosystem and other environmental processes, accurate and up-to-date information on agricultural land use is required. But agricultural land use mapping (i.e. mapping the spatial distribution of crops and croplands) in West Africa has been challenging due to the unavailability of adequate satellite images (as a result of excessive cloud cover), small agricultural fields and a heterogeneous landscape. This study, therefore, investigated the possibilities of improving agricultural land use mapping by utilizing optical satellite images with higher spatial and temporal resolution as well as images from Synthetic Aperture Radar (SAR) systems which are near-independent of weather conditions. The study was conducted at both watershed and regional scales. At watershed scale, classification of different crop types in three watersheds in Ghana, Burkina Faso and Benin was conducted using multi-temporal: (1) only optical images (RapidEye) and (2) optical plus dual polarimetric (VV/VH) SAR images (TerraSAR-X). In addition, inter-annual or short term (2-3 years) changes in cropland area in the past ten years were investigated using historical Landsat images. Results obtained indicate that the use of only optical images to map different crop types in West Africa can achieve moderate classification accuracies (57% to 71%). Overlaps between the cropping calendars of most crops types and certain inter-croppings pose a challenge to optical images in achieving an adequate separation between those crop classes. Integration of SAR images, however, can improve classification accuracies by between 8 and 15%, depending on the number of available images and their acquisition dates. The sensitivity of SAR systems to different crop canopy architectures and land surface characteristics improved the separation between certain crop types. The VV polarization of TerraSAR-X was found to better discrimination between crop types than the VH. Images acquired between August and October were found to be very useful for crop mapping in the sub-region due to structural differences in some crop types during this period. At the regional scale, inter-annual or short term changes in cropland area in the Sudanian Savanna agro-ecological zone in West Africa were assessed by upscaling historical cropland information derived at the watershed scale (using Landsat imagery) unto a coarse spatial resolution, but geographically large, satellite imagery (MODIS) using regression based modeling. The possibility of using such regional scale cropland information to improve government-derived agricultural statistics was investigated by comparing extracted cropland area from the fractional cover maps with district-level agricultural statistics from Ghana The accuracy of the fractional cover maps (MAE between 14.2% and 19.1%) indicate that the heterogeneous agricultural landscape of West Africa can be suitably represented at the regional or continental scales by estimating fractional cropland cover on low resolution Analysis of the results revealed that cropland area in the Sudanian Savanna zone has experienced inter-annual or short term fluctuations in the past ten years due to a variety of factors including climate factors (e.g. floods and droughts), declining soil fertility, population increases and agricultural policies such as fertilizer subsidies. Comparison of extracted cropland area from the fractional cover maps with government's agricultural statistics (MoFA) for seventeen districts (second administrative units) in Ghana revealed high inconsistencies in the government statistics, and highlighted the potential of satellite derived cropland information at regional scales to improve national/sub-national agricultural statistics in West Africa. The results obtained in this study is promising for West Africa, considering the recent launch of optical (Landsat 8) and SAR sensors (Sentinel-1) that will provide free data for crop mapping in the sub-region. This will improve chances of obtaining adequate satellite images acquired during the cropping season for agricultural land use mapping and bolster opportunities of operationalizing agricultural land use mapping in West Africa. This can benefit a wide range of biophysical and economic models and improve decision making based on their results. N2 - Das schnelle Bevölkerungswachstum im Westen Afrikas hat, durch das erhöhte Bedürfnis nach Lebensmittel der expandierenden Bevölkerung, zu einer steigenden Lebensmittelnachfrage und damit zur Ausweitung von Ackerland geführt. Diese Expansionen haben negative Einflüsse auf das Ökosystem der Subregion, die Konsequenzen für Wasser- und Bodenqualität, sowie für Biodiversität und das Klima nach sich ziehen. Um die Veränderungen der Ackerflächen überwachen und die Folgen für das Ökosystem und anderer Umweltprozesse richtig abschätzen zu können, werden genaue und aktuelle Informationen über die landwirtschaftliche Nutzung benötigt. Das kartographieren landwirtschaftlicher Flächennutzung (z.B. das Abbilden der räumlichen Verteilung von Feldfrüchten und Ackerflächen) in Westafrika wurde durch die mangelhafte Verfügbarkeit geeigneter Satellitendaten (durch das Auftreten massiver Wolkenbedeckung), der geringen Größe der landwirtschaftlichen Flächen, sowie der heterogenen Landschaft, erschwert. Aus diesen Gründen untersucht diese Studie das Potential landwirtschaftlich genutzte Flächen, durch die Nutzung von optischen Satellitensystemen mit höherer geometrischer und temporaler Auflösung und Aufnahmen des Synthetic Aperture Radar (SAR) als ein nahezu wetterunabhängiges System, aufzunehmen. Diese Studie wurde sowohl auf der Skala von Wassereinzugsgebieten als auch von Regionen erstellt (Agrarökologische Zone in der sudanesischen Savanne). Auf der Skala der Wassereinzugsgebiete wurden Klassifikationen verschiedener Feldfrüchte in drei Einzugsgebieten in Ghana, Burkina Faso und Benin, mithilfe multitemporaler Abbildungen, bestehend aus entweder (1) nur optischer Abbildungen (Rapideye) oder (2) optischer und dual polarimetric (VV/VH) SAR Aufnahmen (TerraSAR-X), durchgeführt. Zusätzlich wurden interannuelle oder kurzzeitige (2-3 Jahre) Veränderungen in der Ausdehnung von Ackerflächen über die vergangenen zehn Jahre hinweg mittels historischer Landsataufnahmen untersucht. Die Ergebnisse zeigen, dass das Kartographieren verschiedener Feldfrüchte in Westafrika durch nur optische Abbildungen eine moderate Klassifikationsgenauigkeit von 57% bis 71% widergibt. Überlappungen zwischen dem Anbauplan der meisten Feldfrüchte und dem Zwischenfruchtanbau stellen eine Herausforderung für optische Abbildungen dar, um eine angemessene Unterscheidung der Feldfrüchte zu erreichen. Die Hinzunahme von SAR Aufnahmen kann die Klassifikationsgenauigkeit, abhängig von der Anzahl verfügbarer Szenen und deren Aufnahmedatum, jedoch um 8% bis 15% erhöhen. Die Empfindlichkeit der SAR Systeme gegenüber unterschiedlichem Aufbau der Fläche von Feldfrüchten und der Charakteristika der Landoberfläche verbesserten die Trennbarkeit unterschiedlicher Feldfrüchte. Wie sich herausstellte hat die VV Polarisation von TerraSAR-X eine bessere Trennung der Feldfrüchte bewirkt als die VH Polarisation. Außerdem zeigt sich, dass Aufnahmen zwischen August und Oktober sehr nützlich zur Abbildung von Feldfrüchten in der Subregion sind, da in dieser Zeit strukturelle Unterschiede einiger Feldfrüchte beobachtet werden können. Auf regionalem Maßstab wurden interanuelle oder kurzzeitige Veränderungen der Ackerflächen in der agrarökologischen Zone der sudanesischen Savanne in Westafrika durch das hochskalieren historischer Informationen zu den Ackerflächen auf der Skala der Wassereinzugsgebiete (aus Landsat Aufnahmen) auf eine gröbere Auflösung, aber geographisch weite Satellitenszene (MODIS), unter Zuhilfenahme eines auf Regression basierenden Modells, berechnet. Die Möglichkeit regionalskalierte Informationen zu Ackerflächen zu nutzen um staatlich erstellte landwirtschaftliche Statistiken zu verbessern wurde untersucht, indem die extrahierte Fläche der Äcker aus fraktionierten Karten zur Landbedeckung mit landwirtschaftlichen Statistiken auf Distrikt-Level miteinander verglichen werden. Die Genauigkeit der fraktionierten Landbedeckungskarten (Mean Absolute Error zwischen 14,2% und 19,1%) weist darauf hin, dass die heterogene Agrarlandschaft Westafrikas auf einem regionalen oder kontinentalen Maßstab, durch die Abschätzung fraktionierter Ackerflächen aus gering aufgelösten Satellitendaten, angemessen repräsentiert werden kann. Die Analyse der Ergebnisse zeigt, dass die Ackerflächen der Agrarökologischen Zone der sudanesischen Savanne interannuelle oder kurzzeitige Schwankungen in den vergangenen zehn Jahren unterlegen waren. Diese Schwankungen ergeben sich durch bestimmte Faktoren wie: klimatische Faktoren (z.B. Überschwemmungen oder Dürren), sinkende Bodenfruchtbarkeit, Bevölkerungswachstum und landwirtschaftliche Politik wie der Subvention von Düngemitteln. Der Vergleich von fraktionierter Landbedeckungskarten mit staatlich erstellten landwirtschaftlichen Statistiken (MoFA) in 17 Distrikten in Ghana ergaben große Unregelmäßigkeiten in den staatlichen Statistiken und zeigten das Potential von aus Satellitendaten abgeleiteten Informationen zu Ackerflächen auf regionalem Maßstab um nationale oder subnationale landwirtschaftliche Statistiken in Westafrika zu verbessern. Angesichts der baldigen Starts der optischen (Landsat 8) und SAR (Sentinel-1) Sensoren, die frei zugängliche Daten für die Kartierung von Feldfrüchten in der Subregion liefern werden, sind die Ergebnisse, die in dieser Studie gewonnen wurden, vielversprechend für Westafrika. Dadurch steigen die Chancen, dass adäquate Satellitenszenen für das Abbilden landwirtschaftlicher Landnutzung während der Anbauzeitraums bezogen und operationalisiert werden können. Dies hat zur Folge, dass ein breites Spektrum biophysikalischer und ökonomischer Modelle davon profitieren und die Entscheidungsfindung durch die Ergebnisse optimiert wird. KW - Westafrika KW - Fernerkundung KW - Bodennutzung KW - Kartierung KW - Agricultural land use KW - Remote sensing KW - West Africa Y1 - 2014 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-108687 ER -