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Cocoa agroforests sustain ecosystem services (ESs) to varying degrees. These services are otherwise mostly provided by other non-cocoa shade or companion trees. However, the density of shade trees is associated with services and/or disservices that drive farm-specific tree management successions. Considering the growing impacts of climate crisis on farm productivity and the need for adaptation strategies, the ESs are increasingly provisional and contingent on the prevailing vegetation, land tenure, and management successions, amongst others social and ecological factors. To assess the temporal changes in shade management, we surveyed an age gradient of “family farms” in cocoa agroforests created from forest (fCAFS) and savannah (sCAFS) land cover. We evaluated the temporal changes in farm structure, relative tree abundance, and live aboveground biomass of the major canopy strata. We used a spatial point process and linear mixed effect analysis to assess the contributions of associated perennial trees (AsT) on farm rejuvenation patterns. The density of cocoa trees was inconsistent with farm age; this was significantly high on farms in sCAFS (1544 trees ha
Frederick Numbisi; Dieudonne Alemagi; Ann Degrande; Frieke Van Coillie. Farm Rejuvenation-Induced Changes in Tree Spatial Pattern and Live Biomass Species of Cocoa Agroforests in Central Cameroon: Insights for Tree Conservation Incentives in Cocoa Landscapes. Sustainability 2021, 13, 8483 .
AMA StyleFrederick Numbisi, Dieudonne Alemagi, Ann Degrande, Frieke Van Coillie. Farm Rejuvenation-Induced Changes in Tree Spatial Pattern and Live Biomass Species of Cocoa Agroforests in Central Cameroon: Insights for Tree Conservation Incentives in Cocoa Landscapes. Sustainability. 2021; 13 (15):8483.
Chicago/Turabian StyleFrederick Numbisi; Dieudonne Alemagi; Ann Degrande; Frieke Van Coillie. 2021. "Farm Rejuvenation-Induced Changes in Tree Spatial Pattern and Live Biomass Species of Cocoa Agroforests in Central Cameroon: Insights for Tree Conservation Incentives in Cocoa Landscapes." Sustainability 13, no. 15: 8483.
A reliable estimation and monitoring of tree canopy cover or shade distribution is essential for a sustainable cocoa production via agroforestry systems. Remote sensing (RS) data offer great potential in retrieving and monitoring vegetation status at landscape scales. However, parallel advancements in image processing and analysis are required to appropriately use such data for different targeted applications. This study assessed the potential of Sentinel-1A (S-1A) C-band synthetic aperture radar (SAR) backscatter in estimating canopy cover variability in cocoa agroforestry landscapes. We investigated two landscapes, in Center and South Cameroon, which differ in predominant vegetation: forest-savannah transition and forest landscape, respectively. We estimated canopy cover using in-situ digital hemispherical photographs (DHPs) measures of gap fraction, verified the relationship with SAR backscatter intensity and assessed predictions based on three machine learning approaches: multivariate bootstrap regression, neural networks regression, and random forest regression. Our results showed that about 30% of the variance in canopy gap fraction in the cocoa production landscapes was shared by the used SAR backscatter parameters: a combination of S-1A backscatter intensity, backscatter coefficients, difference, cross ratios, and normalized ratios. Based on the model predictions, the VV (co-polarization) backscatter showed high importance in estimating canopy gap fraction; the VH (cross-polarized) backscatter was less sensitive to the estimated canopy gap. We observed that a combination of different backscatter variables was more reliable at predicting the canopy gap variability in the considered type of vegetation in this study—agroforests. Semi-variogram analysis of canopy gap fraction at the landscape scale revealed higher spatial clustering of canopy gap, based on spatial correlation, at a distance range of 18.95 m in the vegetation transition landscape, compared to a 51.12 m spatial correlation range in the forest landscape. We provide new insight on the spatial variability of canopy gaps in the cocoa landscapes which may be essential for predicting impacts of changing and extreme (drought) weather conditions on farm management and productivity. Our results contribute a proof-of-concept in using current and future SAR images to support management tools or strategies on tree inventorying and decisions regarding incentives for shade tree retention and planting in cocoa landscapes.
Frederick Numbisi; Frieke Van Coillie. Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon. Remote Sensing 2020, 12, 4163 .
AMA StyleFrederick Numbisi, Frieke Van Coillie. Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon. Remote Sensing. 2020; 12 (24):4163.
Chicago/Turabian StyleFrederick Numbisi; Frieke Van Coillie. 2020. "Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon." Remote Sensing 12, no. 24: 4163.
The European Space Agency’s Sentinel-1 constellation provides timely and freely available dual-polarized C-band Synthetic Aperture Radar (SAR) imagery. The launch of these and other SAR sensors has boosted the field of SAR-based flood mapping. However, flood mapping in vegetated areas remains a topic under investigation, as backscatter is the result of a complex mixture of backscattering mechanisms and strongly depends on the wave and vegetation characteristics. In this paper, we present an unsupervised object-based clustering framework capable of mapping flooding in the presence and absence of flooded vegetation based on freely and globally available data only. Based on a SAR image pair, the region of interest is segmented into objects, which are converted to a SAR-optical feature space and clustered using K-means. These clusters are then classified based on automatically determined thresholds, and the resulting classification is refined by means of several region growing post-processing steps. The final outcome discriminates between dry land, permanent water, open flooding, and flooded vegetation. Forested areas, which might hide flooding, are indicated as well. The framework is presented based on four case studies, of which two contain flooded vegetation. For the optimal parameter combination, three-class F1 scores between 0.76 and 0.91 are obtained depending on the case, and the pixel- and object-based thresholding benchmarks are outperformed. Furthermore, this framework allows an easy integration of additional data sources when these become available.
Lisa Landuyt; Niko Verhoest; Frieke Van Coillie. Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery. Remote Sensing 2020, 12, 3611 .
AMA StyleLisa Landuyt, Niko Verhoest, Frieke Van Coillie. Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery. Remote Sensing. 2020; 12 (21):3611.
Chicago/Turabian StyleLisa Landuyt; Niko Verhoest; Frieke Van Coillie. 2020. "Flood Mapping in Vegetated Areas Using an Unsupervised Clustering Approach on Sentinel-1 and -2 Imagery." Remote Sensing 12, no. 21: 3611.
Insights into flood dynamics, rather than solely flood extent, are critical for effective flood disaster management, in particular in the context of emergency relief and damage assessment. Although flood dynamics provide insight in the spatio-temporal behaviour of a flood event, to date operational visualization tools are scarce or even non-existent. In this letter, we distil a flood dynamics map from a radar satellite image time series (SITS). For this, we have upscaled and refined an existing design that was originally developed on a small area, describing flood dynamics using an object-based approach and a graph-based representation. Two case studies are used to demonstrate the operational value of this method by visualizing flood dynamics which are not visible on regular flood extent maps. Delineated water bodies are grouped into graphs according to their spatial overlap on consecutive timesteps. Differences in area and backscatter are used to quantify the amount of variation, resulting in a global variation map and a temporal profile for each water body, visually describing the evolution of the backscatter and number of polygons that make up the water body. The process of upscaling led us to applying a different water delineation approach, a different way of ensuring the minimal mapping unit and an increased code efficiency. The framework delivers a new way of visualizing floods, which is straightforward and efficient. Produced global variation maps can be applied in a context of data assimilation and disaster impact management.
Bos Debusscher; Lisa Landuyt; Frieke Van Coillie. A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach. Remote Sensing 2020, 12, 2118 .
AMA StyleBos Debusscher, Lisa Landuyt, Frieke Van Coillie. A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach. Remote Sensing. 2020; 12 (13):2118.
Chicago/Turabian StyleBos Debusscher; Lisa Landuyt; Frieke Van Coillie. 2020. "A Visualization Tool for Flood Dynamics Monitoring Using a Graph-Based Approach." Remote Sensing 12, no. 13: 2118.
Urban residents are exposed to higher levels of heat stress in comparison to the rural population. As this phenomenon could be enhanced by both global greenhouse gas emissions (GHG) and urban expansion, urban planners and policymakers should integrate both in their assessment. One way to consider these two concepts is by using urban climate models at a high resolution. In this study, the influence of urban expansion and GHG emission scenarios is evaluated at 100 m spatial resolution for the city of Brussels (Belgium) in the near (2031-2050) and far (2081-2100) future. Two possible urban planning scenarios (translated into local climate zones, LCZs) in combination with two representative concentration pathways (RCPs 4.5 and 8.5) have been implemented in the urban climate model UrbClim. The projections show that the influence of GHG emissions trumps urban planning measures in each period. In the near future, no large differences are seen between the RCP scenarios; in the far future, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ type, heat stress is projected to increase by a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk, most notably for the North Western part of the Brussels Capital Region. The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in cities.
Marie-Leen Verdonck; Matthias Demuzere; Hans Hooyberghs; Frederik Priem; Frieke Van Coillie. Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios. Journal of Environmental Management 2019, 249, 109210 .
AMA StyleMarie-Leen Verdonck, Matthias Demuzere, Hans Hooyberghs, Frederik Priem, Frieke Van Coillie. Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios. Journal of Environmental Management. 2019; 249 ():109210.
Chicago/Turabian StyleMarie-Leen Verdonck; Matthias Demuzere; Hans Hooyberghs; Frederik Priem; Frieke Van Coillie. 2019. "Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios." Journal of Environmental Management 249, no. : 109210.
The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool for flood mapping from space. An object-based approach can limit the necessary computer power and computation time, while a graph-based approach allows for a comprehensible interpretation of dynamics. This method proves to be a useful tool to gain insight in a flood event. Graph representation helps to identify and locate entities within the study site and describe their evolution throughout the time series.
Bos Debusscher; Frieke Van Coillie. Object-Based Flood Analysis Using a Graph-Based Representation. Remote Sensing 2019, 11, 1883 .
AMA StyleBos Debusscher, Frieke Van Coillie. Object-Based Flood Analysis Using a Graph-Based Representation. Remote Sensing. 2019; 11 (16):1883.
Chicago/Turabian StyleBos Debusscher; Frieke Van Coillie. 2019. "Object-Based Flood Analysis Using a Graph-Based Representation." Remote Sensing 11, no. 16: 1883.
Over the last decade, Kunming has been subject to a strong urbanisation driven by rapid economic growth and socio-economic, topographical and proximity factors. As this urbanisation is expected to continue in the future, it is important to understand its environmental impacts and the role that spatial planning strategies and urbanisation regulations can play herein. This is addressed by (1) quantifying the cities’ expansion and intra-urban restructuring using Local Climate Zones (LCZs) for three periods in time (2005, 2011 and 2017) based on the World Urban Database and Access Portal Tool (WUDAPT) protocol, and (2) cross-referencing observed land-use and land-cover changes with existing planning regulations. The results of the surveys on urban development show that, between 2005 and 2011, the city showed spatial expansion, whereas between 2011 and 2017, densification mainly occurred within the existing urban extent. Between 2005 and 2017, the fraction of open LCZs increased, with the largest increase taking place between 2011 and 2017. The largest decrease was seen for low the plants (LCZ D) and agricultural greenhouse (LCZ H) categories. As the potential of LCZs as, for example, a heat stress assessment tool has been shown elsewhere, understanding the relation between policy strategies and LCZ changes is important to take rational urban planning strategies toward sustainable city development.
Stéphanie Vandamme; Matthias Demuzere; Marie-Leen Verdonck; Zhiming Zhang; Frieke Van Coillie. Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones. Remote Sensing 2019, 11, 1731 .
AMA StyleStéphanie Vandamme, Matthias Demuzere, Marie-Leen Verdonck, Zhiming Zhang, Frieke Van Coillie. Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones. Remote Sensing. 2019; 11 (14):1731.
Chicago/Turabian StyleStéphanie Vandamme; Matthias Demuzere; Marie-Leen Verdonck; Zhiming Zhang; Frieke Van Coillie. 2019. "Revealing Kunming’s (China) Historical Urban Planning Policies Through Local Climate Zones." Remote Sensing 11, no. 14: 1731.
Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories.
Frederick N. Numbisi; Frieke M. B. Van Coillie; Robert De Wulf. Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. ISPRS International Journal of Geo-Information 2019, 8, 179 .
AMA StyleFrederick N. Numbisi, Frieke M. B. Van Coillie, Robert De Wulf. Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. ISPRS International Journal of Geo-Information. 2019; 8 (4):179.
Chicago/Turabian StyleFrederick N. Numbisi; Frieke M. B. Van Coillie; Robert De Wulf. 2019. "Delineation of Cocoa Agroforests Using Multiseason Sentinel-1 SAR Images: A Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping." ISPRS International Journal of Geo-Information 8, no. 4: 179.
Since 2012, Local Climate Zones (LCZ) have been used for numerous studies related to urban environment. In 2015, this use amplified because a method to map urban areas in LCZs was introduced by the World Urban Database and Access Portal Tools (WUDAPT). However in 2017, the first HUMan INfluence EXperiment showed that these maps often have poor or low quality. Since the maps are used in different applications such as urban modelling and land use/land cover change studies, it is of the utmost importance to improve mapping accuracies and a second experiment was launched. In HUMINEX 2.0, the focus lies on providing guidelines on the use of the mapping protocol based on the results of both HUMINEX 1.0 and 2.0. The results showed that: (1) it is important to follow the mapping protocol as strictly as possible, (2) a reasonable amount of time should be spent on the mapping procedure, (3) all users should perform a driving test, and (4) training area sets should be stored in the WUDAPT database for other users.
Marie-Leen Verdonck; Matthias Demuzere; Benjamin Bechtel; Christoph Beck; Oscar Brousse; Arjan Droste; Daniel Fenner; François Leconte; Frieke Van Coillie. The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification. Urban Science 2019, 3, 27 .
AMA StyleMarie-Leen Verdonck, Matthias Demuzere, Benjamin Bechtel, Christoph Beck, Oscar Brousse, Arjan Droste, Daniel Fenner, François Leconte, Frieke Van Coillie. The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification. Urban Science. 2019; 3 (1):27.
Chicago/Turabian StyleMarie-Leen Verdonck; Matthias Demuzere; Benjamin Bechtel; Christoph Beck; Oscar Brousse; Arjan Droste; Daniel Fenner; François Leconte; Frieke Van Coillie. 2019. "The Human Influence Experiment (Part 2): Guidelines for Improved Mapping of Local Climate Zones Using a Supervised Classification." Urban Science 3, no. 1: 27.
Delineating the cropping area of cocoa agroforests is a major challenge for quantifying the contribution of the land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multi-spectral optical images is difficult due to a similarity in the spectral characteristics of their canopy; moreover, optical sensors are largely impeded by the frequent cloud cover in the tropics. This study explores multi-season Sentinel-1 C-band SAR image to discriminate cocoa agroforests from transition forests for a heterogeneous landscape in central Cameroon. We use an ensemble classifier, random forest, to average SAR image texture features of GLCM (Grey Level Co-occurrence Matrix) across seasons; next, we compare classification performance with results from RapidEye optical data. Moreover, we assess the performance of GLCM texture feature extraction at four different grey level quantization: 32bits, 8bits, 6bits, and 4bits. The classification overall accuracy (OA) of texture-based maps outperformed that from an optical image; the highest OA of 88.8% was recorded at 6bits grey level. This quantization level, in comparison to the initial 32bits in SAR images, reduced the class prediction error by 2.9%. Although this prediction gain may be large for the landscape area, the resultant thematic map reveals the decrease and fragmentation of forest cover by cocoa agroforests. According to our classification validation, the Shannon entropy (H) or uncertainty provides a reliable validation for class predictions and reveals detail inference for discriminating inherently heterogeneous vegetation categories. The texture-based classification achieved a reliable accuracy considering the heterogeneity of the landscape and vegetation classes.
Frederick N. Numbisi; Frieke M. B. Van Coillie; Robert De Wulf. Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. 2019, 1 .
AMA StyleFrederick N. Numbisi, Frieke M. B. Van Coillie, Robert De Wulf. Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping. . 2019; ():1.
Chicago/Turabian StyleFrederick N. Numbisi; Frieke M. B. Van Coillie; Robert De Wulf. 2019. "Delineation of Cocoa Agroforests Using Multi-Season Sentinel-1 SAR Images: Low Grey Level Range Reduces Uncertainties in GLCM Texture-Based Mapping." , no. : 1.
High population densities in cities and rapid urban growth increase the vulnerability of the urban environment to extreme weather events. Urban planning should account for these extreme events as efficiently as possible. One way is to locate hot spots in an urban environment by mapping cities into local climate zones (LCZ) and evaluate heat stress related to these zones. LCZs are likely to become a standard in urban climate modelling as they capture important urban morphological characteristics. For instance, temperature regimes linked to spatially explicit LCZ maps should be assessed for all LCZ zones derived from these maps. This study assesses the thermal behavior of mapped LCZs using simulated temperature data from the UrbClim model. Prior to temperature analysis, the model was validated with observational data. To evaluate the robustness of the analysis, we ran the model in three cities in Belgium: Antwerp, Brussels, and Ghent. The results show that temperature regimes are significantly different for all the built zones in the urban environment independent of the city. Second, the susceptibility to heat stress can differ greatly depending on the zone. The unique thermal behavior of the different LCZs provides indispensable information on the urban environment and its climatic conditions. This study shows that the LCZ scheme has a potential to help urban planners globally tackle adverse effects of extreme weather events.
Marie-Leen Verdonck; Matthias Demuzere; Hans Hooyberghs; Christoph Beck; Josef Cyrys; Alexandra Schneider; Robert Dewulf; Frieke Van Coillie. The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data. Landscape and Urban Planning 2018, 178, 183 -197.
AMA StyleMarie-Leen Verdonck, Matthias Demuzere, Hans Hooyberghs, Christoph Beck, Josef Cyrys, Alexandra Schneider, Robert Dewulf, Frieke Van Coillie. The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data. Landscape and Urban Planning. 2018; 178 ():183-197.
Chicago/Turabian StyleMarie-Leen Verdonck; Matthias Demuzere; Hans Hooyberghs; Christoph Beck; Josef Cyrys; Alexandra Schneider; Robert Dewulf; Frieke Van Coillie. 2018. "The potential of local climate zones maps as a heat stress assessment tool, supported by simulated air temperature data." Landscape and Urban Planning 178, no. : 183-197.
In our changing world, floods are a threat of increasing concern. Within this context, flood mapping is important for both damage assessment and forecast improvement. Due to the suitability of synthetic aperture radar (SAR) for flood mapping, a broad range of SAR-based flood mapping algorithms has been developed during the past years. However, most of these algorithms were presented based on a single test case only and comparisons between methods are rare. This paper presents an in-depth assessment and comparison of the established pixel-based flood mapping approaches, including global and enhanced thresholding, active contour modeling and change detection. The methods were tested on medium-resolution SAR images of different flood events and lakes across the U.K. and Ireland and were evaluated on both accuracy and robustness. Results indicate that the most suited method depends on the area of interest and its characteristics as well as the intended use of the observation product. Due to its high robustness and good performance, tiled thresholding is suited for automated, near-real time flood detection and monitoring. Active contour models can provide higher accuracies but require long computation times that strongly increase with increasing image sizes, making them more appropriate for accurate flood mapping in smaller areas of interest.
Lisa Landuyt; Alexandra Van Wesemael; Guy J.-P. Schumann; Renaud Hostache; Niko E. C. Verhoest; Frieke M. B. Van Coillie. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches. IEEE Transactions on Geoscience and Remote Sensing 2018, 57, 722 -739.
AMA StyleLisa Landuyt, Alexandra Van Wesemael, Guy J.-P. Schumann, Renaud Hostache, Niko E. C. Verhoest, Frieke M. B. Van Coillie. Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches. IEEE Transactions on Geoscience and Remote Sensing. 2018; 57 (2):722-739.
Chicago/Turabian StyleLisa Landuyt; Alexandra Van Wesemael; Guy J.-P. Schumann; Renaud Hostache; Niko E. C. Verhoest; Frieke M. B. Van Coillie. 2018. "Flood Mapping Based on Synthetic Aperture Radar: An Assessment of Established Approaches." IEEE Transactions on Geoscience and Remote Sensing 57, no. 2: 722-739.
Marie-Leen Verdonck; Akpona Okujeni; Sebastian van der Linden; Matthias Demuzere; Robert De Wulf; Frieke Van Coillie. Influence of neighbourhood information on ‘Local Climate Zone’ mapping in heterogeneous cities. International Journal of Applied Earth Observation and Geoinformation 2017, 62, 102 -113.
AMA StyleMarie-Leen Verdonck, Akpona Okujeni, Sebastian van der Linden, Matthias Demuzere, Robert De Wulf, Frieke Van Coillie. Influence of neighbourhood information on ‘Local Climate Zone’ mapping in heterogeneous cities. International Journal of Applied Earth Observation and Geoinformation. 2017; 62 ():102-113.
Chicago/Turabian StyleMarie-Leen Verdonck; Akpona Okujeni; Sebastian van der Linden; Matthias Demuzere; Robert De Wulf; Frieke Van Coillie. 2017. "Influence of neighbourhood information on ‘Local Climate Zone’ mapping in heterogeneous cities." International Journal of Applied Earth Observation and Geoinformation 62, no. : 102-113.
The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets.
Benjamin Bechtel; Matthias Demuzere; Panagiotis Sismanidis; Daniel Fenner; Oscar Brousse; Christoph Beck; Frieke Van Coillie; Olaf Conrad; Iphigenia Keramitsoglou; Ariane Middel; Gerald Mills; Dev Niyogi; Marco Otto; Linda See; Marie-Leen Verdonck. Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX). Urban Science 2017, 1, 15 .
AMA StyleBenjamin Bechtel, Matthias Demuzere, Panagiotis Sismanidis, Daniel Fenner, Oscar Brousse, Christoph Beck, Frieke Van Coillie, Olaf Conrad, Iphigenia Keramitsoglou, Ariane Middel, Gerald Mills, Dev Niyogi, Marco Otto, Linda See, Marie-Leen Verdonck. Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX). Urban Science. 2017; 1 (2):15.
Chicago/Turabian StyleBenjamin Bechtel; Matthias Demuzere; Panagiotis Sismanidis; Daniel Fenner; Oscar Brousse; Christoph Beck; Frieke Van Coillie; Olaf Conrad; Iphigenia Keramitsoglou; Ariane Middel; Gerald Mills; Dev Niyogi; Marco Otto; Linda See; Marie-Leen Verdonck. 2017. "Quality of Crowdsourced Data on Urban Morphology—The Human Influence Experiment (HUMINEX)." Urban Science 1, no. 2: 15.
Soil erosion and desertification are the main problems faced by the Bou-Hedma National Park in South-Tunisia. Restoration of the original woodland cover, particularly by afforestation and reforestation with Acacia tortilis ssp. raddiana, has been recognized as efficient to combat further degradation of the environment in this protected area. In order to study effects of woodland restoration and future trends in Bou-Hedma, it is essential to accurately assess its current situation. This paper addresses a monotemporal assessment of the population structure of A. tortilis. An extensive field inventory was performed to provide deeper insight into the dendrometric characteristics of this keystone species. Next, a spatially explicit and repeatable method is developed to model key tree attributes like crown diameter, volume and tree height from which the structural composition of the A. tortilis population in the Bou Hedma National Park is derived. This method involves analysis of a very high resolution (VHR) GeoEye-1 image within an OBIA (Object-based Image Analysis) framework. The remote sensing (RS) results show that the population of A. tortilis is typified by an irregular population structure and confirm the findings of Noumi and Chaieb (2012) suggesting regressive population dynamics. The RS approach demonstrates potential for monitoring purposes in this particular setting of an arid environment.
Frieke van Coillie; Kevin Delaplace; Donald Gabriels; Koen de Smet; Mohammed Ouessar; Azaiez Ouled Belgacem; Houcine Taamallah; Robert de Wulf. Monotemporal assessment of the population structure of Acacia tortilis (Forssk.) Hayne ssp. raddiana (Savi) Brenan in Bou Hedma National Park, Tunisia: A terrestrial and remote sensing approach. Journal of Arid Environments 2016, 129, 80 -92.
AMA StyleFrieke van Coillie, Kevin Delaplace, Donald Gabriels, Koen de Smet, Mohammed Ouessar, Azaiez Ouled Belgacem, Houcine Taamallah, Robert de Wulf. Monotemporal assessment of the population structure of Acacia tortilis (Forssk.) Hayne ssp. raddiana (Savi) Brenan in Bou Hedma National Park, Tunisia: A terrestrial and remote sensing approach. Journal of Arid Environments. 2016; 129 ():80-92.
Chicago/Turabian StyleFrieke van Coillie; Kevin Delaplace; Donald Gabriels; Koen de Smet; Mohammed Ouessar; Azaiez Ouled Belgacem; Houcine Taamallah; Robert de Wulf. 2016. "Monotemporal assessment of the population structure of Acacia tortilis (Forssk.) Hayne ssp. raddiana (Savi) Brenan in Bou Hedma National Park, Tunisia: A terrestrial and remote sensing approach." Journal of Arid Environments 129, no. : 80-92.
Nowadays, advanced technology in remote sensing allows us to get multi-sensor and multi-resolution data from the same region. Fusion of these data sources for classification remains challenging problems. In this paper, we propose a novel algorithm for hyperspectral (HS) image pansharpening with two-stage guided filtering in PCA (principal component analysis) domain. In the first stage, we first downsample the high-resolution RGB image to the same spatial resolution of original low-resolution HS image, and use guided filter to transfer the image details (e.g. edge) of the downsampled RGB image to the original HS image in the PCA domain. In the second stage, we perform upsampling on the resulting HS image from the first stage by using original high-resolution RGB image and guided filter in PCA domain. This yields a clear improvement over an older approach with one stage guided filtering in PCA domain. Experimental results on fusion of a low spatial-resolution Thermal Infrared HS image and a high spatial-resolution visible RGB image from the 2014 IEEE GRSS Data Fusion Contest, are very encouraging.
Wenzhi Liao; Xin Huang; Frieke Van Coillie; Guy Thoonen; Aleksandra Pizurica; Paul Scheunders; Wilfried Philips. Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) 2015, 1 -4.
AMA StyleWenzhi Liao, Xin Huang, Frieke Van Coillie, Guy Thoonen, Aleksandra Pizurica, Paul Scheunders, Wilfried Philips. Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter. 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 2015; ():1-4.
Chicago/Turabian StyleWenzhi Liao; Xin Huang; Frieke Van Coillie; Guy Thoonen; Aleksandra Pizurica; Paul Scheunders; Wilfried Philips. 2015. "Two-stage fusion of thermal hyperspectral and visible RGB image by PCA and guided filter." 2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS) , no. : 1-4.
The visualization of vector occurrence in space and time is an important aspect of studying vector-borne diseases. Detailed maps of possible vector habitats provide valuable information for the prediction of infection risk zones but are currently lacking for most parts of the world. Nonetheless, monitoring vector habitats from the finest scales up to farm level is of key importance to refine currently existing broad-scale infection risk models. Using Fasciola hepatica, a parasite liver fluke, as a case in point, this study illustrates the potential of very high resolution (VHR) optical satellite imagery to efficiently and semi-automatically detect detailed vector habitats. A WorldView2 satellite image capable of <5m resolution was acquired in the spring of 2013 for the area around Bruges, Belgium, a region where dairy farms suffer from liver fluke infections transmitted by freshwater snails. The vector thrives in small water bodies (SWBs), such as ponds, ditches and other humid areas consisting of open water, aquatic vegetation and/or inundated grass. These water bodies can be as small as a few m2 and are most often not present on existing land cover maps because of their small size. We present a classification procedure based on object-based image analysis (OBIA) that proved valuable to detect SWBs at a fine scale in an operational and semi-automated way. The classification results were compared to field and other reference data such as existing broad-scale maps and expert knowledge. Overall, the SWB detection accuracy reached up to 87%. The resulting fine-scale SWB map can be used as input for spatial distribution modelling of the liver fluke snail vector to enable development of improved infection risk mapping and management advice adapted to specific, local farm situations.
Els De Roeck; Frieke Van Coillie; Robert De Wulf; Karen Soenen; Johannes Charlier; Jozef Vercruysse; Wouter Hantson; Els Ducheyne; Guy Hendrickx. Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study. Geospatial Health 2014, 8, 671 .
AMA StyleEls De Roeck, Frieke Van Coillie, Robert De Wulf, Karen Soenen, Johannes Charlier, Jozef Vercruysse, Wouter Hantson, Els Ducheyne, Guy Hendrickx. Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study. Geospatial Health. 2014; 8 (3):671.
Chicago/Turabian StyleEls De Roeck; Frieke Van Coillie; Robert De Wulf; Karen Soenen; Johannes Charlier; Jozef Vercruysse; Wouter Hantson; Els Ducheyne; Guy Hendrickx. 2014. "Fine-scale mapping of vector habitats using very high resolution satellite imagery: a liver fluke case-study." Geospatial Health 8, no. 3: 671.
The trematode parasite Fasciola hepatica causes important economic losses in ruminants worldwide. Current spatial distribution models do not provide sufficient detail to support farm-specific control strategies. A technology to reliably assess the spatial distribution of intermediate host snail habitats on farms would be a major step forward to this respect. The aim of this study was to conduct a longitudinal field survey in Flanders (Belgium) to (i) characterise suitable small water bodies (SWB) for Galba truncatula and (ii) describe the population dynamics of G. truncatula. Four F. hepatica-infected farms from two distinct agricultural regions were examined for the abundance of G. truncatula from the beginning (April 2012) until the end (November 2012) of the grazing season. Per farm, 12 to 18 SWB were selected for monthly examination, using a 10 m transect analysis. Observations on G. truncatula abundance were coupled with meteorological and (micro-)environmental factors and the within-herd prevalence of F. hepatica using simple comparison or negative binomial regression models. A total of 54 examined SWB were classified as a pond, ditch, trench, furrow or moist area. G. truncatula abundance was significantly associated with SWB-type, region and total monthly precipitation, but not with monthly temperature. The clear differences in G. truncatula abundance between the 2 studied regions did not result in comparable differences in F. hepatica prevalence in the cattle. Exploration of the relationship of G. truncatula abundance with (micro)-environmental variables revealed a positive association with soil and water pH and the occurrence of Ranunculus sp. and a negative association with mowed pastures, water temperature and presence of reed-like plant species. Farm-level predictions of G. truncatula risk and subsequent risk for F. hepatica occurrence would require a rainfall, soil type (representing the agricultural region) and SWB layer in a geographic information system. While rainfall and soil type information is easily accessible, the recent advances in very high spatial resolution cameras carried on board of satellites, planes or drones should allow the delineation of SWBs in the future.
Johannes Charlier; Karen Soenen; Els De Roeck; Wouter Hantson; Els Ducheyne; Frieke Van Coillie; Robert De Wulf; Guy Hendrickx; Jozef Vercruysse. Longitudinal study on the temporal and micro-spatial distribution of Galba truncatula in four farms in Belgium as a base for small-scale risk mapping of Fasciola hepatica. Parasites & Vectors 2014, 7, 528 .
AMA StyleJohannes Charlier, Karen Soenen, Els De Roeck, Wouter Hantson, Els Ducheyne, Frieke Van Coillie, Robert De Wulf, Guy Hendrickx, Jozef Vercruysse. Longitudinal study on the temporal and micro-spatial distribution of Galba truncatula in four farms in Belgium as a base for small-scale risk mapping of Fasciola hepatica. Parasites & Vectors. 2014; 7 (1):528.
Chicago/Turabian StyleJohannes Charlier; Karen Soenen; Els De Roeck; Wouter Hantson; Els Ducheyne; Frieke Van Coillie; Robert De Wulf; Guy Hendrickx; Jozef Vercruysse. 2014. "Longitudinal study on the temporal and micro-spatial distribution of Galba truncatula in four farms in Belgium as a base for small-scale risk mapping of Fasciola hepatica." Parasites & Vectors 7, no. 1: 528.
Mingyu Yang; Frieke Van Coillie; Min Liu; Robert De Wulf; Luc Hens; Xiaokun Ou. A GIS Approach to Estimating Tourists' Off-road Use in a Mountainous Protected Area of Northwest Yunnan, China. Mountain Research and Development 2014, 34, 107 -117.
AMA StyleMingyu Yang, Frieke Van Coillie, Min Liu, Robert De Wulf, Luc Hens, Xiaokun Ou. A GIS Approach to Estimating Tourists' Off-road Use in a Mountainous Protected Area of Northwest Yunnan, China. Mountain Research and Development. 2014; 34 (2):107-117.
Chicago/Turabian StyleMingyu Yang; Frieke Van Coillie; Min Liu; Robert De Wulf; Luc Hens; Xiaokun Ou. 2014. "A GIS Approach to Estimating Tourists' Off-road Use in a Mountainous Protected Area of Northwest Yunnan, China." Mountain Research and Development 34, no. 2: 107-117.
The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote sensing data, to improve the accuracy of mountain vegetation maps. Two different mountainous areas located in Lancang (Mekong) watershed served as study sites. An Artificial Neural Network (ANN) architecture classification was used as image classification protocol. In addition, canonical correspondence analysis (CCA) ordination was applied to address the relationships between topography and human disturbance factors with the spatial distribution of vegetation patterns. We used ordinary kriging at unobserved locations to predict the CCA scores. The CCA ordination results showed that the vegetation spatial distribution patterns are strongly affected by topography and human disturbance factors. The overall accuracy of vegetation classification was significantly improved by incorporating DEM or four CCA axes as additional channels in both the northern and southern study areas. However, there was no significant difference between using DEM or four CCA axes as extra channels in the northern steep mountainous areas because of a strong redundancy between CCA axes and DEM data. In the southern lower mountainous areas, the accuracy was significantly higher using four CCA axes as extra bands, compared to using DEM as an extra band. In the southern study area, the variance of vegetation data explained by human disturbance factors was larger than the variance explained by topographic attributes.
Zhiming Zhang; Frieke Van Coillie; Xiaokun Ou; Robert De Wulf. Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China. Remote Sensing 2014, 6, 1026 -1056.
AMA StyleZhiming Zhang, Frieke Van Coillie, Xiaokun Ou, Robert De Wulf. Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China. Remote Sensing. 2014; 6 (2):1026-1056.
Chicago/Turabian StyleZhiming Zhang; Frieke Van Coillie; Xiaokun Ou; Robert De Wulf. 2014. "Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China." Remote Sensing 6, no. 2: 1026-1056.