Professor Lex Comber

Profile

My research in Spatial Data Analytics develops methods that integrate and analyse high volumes of spatial data to uncover hidden patterns / correlations. It provides spatial insight for social and environmental applications. The context for this work is the limitless demand for and capture of spatial data: from smartphone apps, social networks and more traditional activities related to planning, human rights, environmental justice, public health, climate change, agriculture, land use, etc.  

Current research is developing models for analysing spatial data generated by GPS-enabled devices in the context of environmental monitoring. This extends concepts of 'crowd sourcing' into the  'citizen sensor', supporting community empowerment, resilience and adaptability. 

Yi-min Chang-Chien (with Steve Carver), Using social media data to develop real-time augmentation to traditional land use mapping (started Sept 2016)

Yuanxuan Yang (with Alison Heppenstall), GIS-based spatial data mining and microsimulation for the analysis of elderly care service inequalities (started Sept 2016)

Jennie Gray (with Lisa Buckner), Predictive Geo-Demographics (started Sept 2017)

Arif Rohman (with Gordon Mitchel;), Evaluating Flood Hazard Impact to Community Resilience in Urban Areas(started Sept 2017)

NERC / Newton Fund: Modelling and Managing Critical Zone Relationships between Soil, Water and Ecosystem Processes Across the Loess Plateau (CI with Rothamsted)

This project on soil loss and erosion in the Loess Plateau of China in the upper and middle reaches of China's Yellow River develops multi-scale approaches to link collected environmental, biological and agronomic data collected by experiment, remote sensing data and modelling approaches.

The Leeds work focusses on a number of areas

1. Land Use classification and change analysis over the whole Loess Plateau.

This will generate maps of land use for each year over the period of analysis (e.g. 2000 to 2015). This will extend a recently developed method for developing time series analyses of MODIS Enhanced Vegetation Index (EVI) data (Tsutsumida et al., 2016; Tsutsumida & Comber, 2015), will generate spatially distributed measures of error and uncertainty for each class and allow land use change to be determined. MODIS has a 250m pixel size and the methodology will allow sub-pixel proportions of individual land use classes to be determined. That is each pixel will not be allocated to one class.

2. Inferring run off, erosion and land degradation at the sub-catchment level land.

The time series land use data will be extracted for subsets of the study area (e.g. sub-catchment, watershed or ecological zone) that the project has a focus on. In these, the locations of specific land use changes associated with degradation related processes will be identified. Specific land use changes are associated with changes in runoff, sediment loss and erosion over specific time frames. For example, areas that have undergone land use conversions from Cropland / Arable to Trees. The specific land use changes will be informed by the nature and location of the land use processes, measurements being examined by WP1 and WP2. The changes in runoff etc., related to land use change from the analysis of the MODIS remote sensing data will be compared to the changes suggested by the models being extensively developed in WP1 and WP2 and used to calibrate the sub-catchment scale models.

3. Large scale modelling of run off, erosion and land degradation.

The calibrated sub-catchment remote sensing models will allow a whole study area model to be developed from remote sensing data. The calibration processes in (2) above will ensure that the remote sensing land use change models reliably predict runoff in the study area sub-sets (sub-catchments etc.). A large scale model, covering the entire study area, will then be developed that infers runoff, etc., in relation to land use change across the whole study area including in previously un-sampled locations. These locations areas will be validated in order to validate the changes in runoff etc., suggested by the model.

Start: January 2016 Duration: 3 Years

 

SARIC (BBSRC / NERC / ESRC): Real-time predictions of pesticide run-off risk which: multi-scale visualisations of water quality risks and costs (PI with Cranfield University, Rothamsted Research, University of Reading (Agrimetrics), Bangor University)

This Research Translation Project develops a proof of concept to tests the value of real-time predictions of agro-chemical run-off risk at two scales of decision making: field scale for on farm decisions about agro-chemical applications risk and catchment scale for water company groundwater abstraction decisions.

Agro-chemicals (fertilisers, pesticides, herbicides, etc) are less effective if they are washed away soon after they are applied. They can also negatively affect ground water quality and the environment. The farmer may have to re-apply the agro-chemical and water companies may have treat groundwater to meet drinking water quality standards, and in some cases change water abstraction locations. For both farmers and water companies additional costs are incurred.

This project develops proofs of concept for 2 web-mapping tools to model the risk associated with agro-chemical applications: a catchment-scale tool to support water company decision making and a field-scale tool to support farmer decision making. Both tools combine live, real-time data from the Met Office on rainfall type and probability with landscape models of underlying soil, landform, drainage, land use etc. in order to model agro-chemical runoff risk. User-groups will feedback their experiences about the operational use and functionality of the tools to provide information for the modelling and programming teams to adjust the background engine and front-end functionality.

The project outputs will include the specification of for national decision tools, targeted at farmers and water companies, to quantify the risks associated with a full set of common agro-chemical applications designed be accessed using desktop PCs and smartphones.

 

Start: January 2017 Duration: 19 months

I convene 3 module in 2017/18: 

  • Big Data and Consumer Analytics
  • Geocomputation & Location Analysis
  • Internship 

Recent Publications

Benitez-Paez F, Comber A, Trilles S and Huerta J (in press). Creating a conceptual framework to assess and improve the re-usability of open geographic data in cities. Paper accepted for publication in Transactions in GIS(April 2018) https://doi.org/10.1111/tgis.12449

Comber Aand Kuhn W (2018). Fuzzy difference and data primitives: a transparent approach for supporting different definitions of forest in the context of REDD+. Paper accepted for publication in Geographica Helvetica,  73: 151-163 https://doi.org/10.5194/gh-73-151-2018, Paper: https://www.geogr-helv.net/73/151/2018/ Data and code at https://doi.org/10.5281/zenodo.1188392

Fu W, Lü Y, Harris P, Comber Aand Wu L (2018). Peri-urbanization may vary with vegetation restoration: A large scale regional analysis. Urban Forestry and Urban Greening, 29: 77-87. https://doi.org/10.1016/j.ufug.2017.11.006

Luo Y, Lü Y, Fu B, Harris P, Wu L and Comber A(2018). When multi-functional landscape meets critical zone science: advancing multi-disciplinary research for sustainable human well-being. National Science Review, https://doi.org/10.1093/nsr/nwy003

Hu, J, Lü Y, Fu B, Comber AJand Harris P (2017). Quantifying the effect of ecological restoration on runoff and sediment yields: A meta-analysis for the Loess Plateau. Progress in Physical Geography, DOI:10.1177/0309133317738710

Harris, R., O’Sullivan, D., Gahegan, M., Charlton, M., Comber, L., Longley, P., Brunsdon, C., Malleson, N., Heppenstall, A., Singleton, A. Arribas-Bel, D. and Evans A (2017). More bark than bytes? Reflections on 21+ years of geocomputation. Environment and Planning B: Urban Analytics and City Science44(4), pp.598-617

Li T, Lü Y, Fu B, Comber AJ, Harris P and Wu L (2017). Gauging policy-driven large-scale vegetation restoration programmes under a changing environment: their effectiveness and socioeconomic relationships. Science of the Total Environment, 607-608: 911-919, DOI: 10.1016/j.scitotenv.2017.07.044

Tsutsumida N, Harris P and Comber A(2017). The application of a geographically weighted principal components analysis for exploring 23 years of goat population change across Mongolia. Annals of the American Association of Geographers, 107(5): 1060-1074, http://dx.doi.org/10.1080/24694452.2017.1309968

Comber A, Brunsdon CF, Charlton M and Harris P (2017). Geographically weighted correspondence matrices for local change analyses and error reporting: mapping the spatial distribution of errors and change. Remote Sensing Letters, 8(3): 234-243, http://dx.doi.org/10.1080/2150704X.2016.1258126

Comber A, Harris P and Tsutsumida N (2016). Improving land cover classification using input variables derived from a geographically weighted principal components analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 347–360, doi:10.1016/j.isprsjprs.2016.06.014.

Comber A, Mooney P, Purves RS,Rocchini D and WalzA (2016). Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover. PlosONE, 11(7): e0158329, DOI: 10.1371/journal.pone.0158329

Comber A, Davies H, Pinder D, Whittow JB, Woodhall A and Johnson SCM (2016). Mapping coastal land use changes 1965-2014: methods for handling historical thematic data. Transactions of the Institute of British Geographers, DOI: 10.1111/tran.12128

See L, Mooney P, Foody G, Bastin L, Comber A, Estima J, Steffen F, Kerle N, Jiang B, Laakso M, Liu HY, Milinski G, Nikši M, Painho m, Pdör A, Olteanu-Raimond AM and Rutzinger M (2016). Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information, ISPRS International Journal of Geo-Information, 5:55

Comber A, Balzter H, Cole B, Johnson S, Oguto B and Fisher P, (2016). Methods to quantify regional differences in land cover change. Remote Sensing,8, 176; doi:10.3390/rs8030176

Comber A, Fonte C, Foody G, Fritz S, Harris P, Raimond A-M and See L, (2016). Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information. GeoInformatica, 20(3): 503-527. Doi: 0.1007/s10707-016-0248-z

 

Journal Papers published or in press

Benitez-Paez F, Comber A, Trilles S and Huerta J (in press). Creating a conceptual framework to assess and improve the re-usability of open geographic data in cities. Paper accepted for publication in Transactions in GIS(April 2018) https://doi.org/10.1111/tgis.12449

Comber Aand Kuhn W (2018). Fuzzy difference and data primitives: a transparent approach for supporting different definitions of forest in the context of REDD+. Paper accepted for publication in Geographica Helvetica,  73: 151-163 https://doi.org/10.5194/gh-73-151-2018 Paper: https://www.geogr-helv.net/73/151/2018/ Data and code at https://doi.org/10.5281/zenodo.1188392

Fu W, Lü Y, Harris P, Comber Aand Wu L (2018). Peri-urbanization may vary with vegetation restoration: A large scale regional analysis. Urban Forestry and Urban Greening, 29: 77-87. https://doi.org/10.1016/j.ufug.2017.11.006

Luo Y, Lü Y, Fu B, Harris P, Wu L and Comber A(2018). When multi-functional landscape meets critical zone science: advancing multi-disciplinary research for sustainable human well-being. National Science Review, https://doi.org/10.1093/nsr/nwy003

Hu, J, Lü Y, Fu B, Comber AJand Harris P (2017). Quantifying the effect of ecological restoration on runoff and sediment yields: A meta-analysis for the Loess Plateau. Progress in Physical Geography, DOI:10.1177/0309133317738710

Harris, R., O’Sullivan, D., Gahegan, M., Charlton, M., Comber, L., Longley, P., Brunsdon, C., Malleson, N., Heppenstall, A., Singleton, A. Arribas-Bel, D. and Evans A (2017). More bark than bytes? Reflections on 21+ years of geocomputation. Environment and Planning B: Urban Analytics and City Science44(4), pp.598-617

Li T, Lü Y, Fu B, Comber AJ, Harris P and Wu L (2017). Gauging policy-driven large-scale vegetation restoration programmes under a changing environment: their effectiveness and socioeconomic relationships. Science of the Total Environment, 607-608: 911-919, DOI: 10.1016/j.scitotenv.2017.07.044

Jega IM and Comber A (2017). A comparison of methods for spatial interpolation across different spatial scales. SSRG International Journal of Geo informatics and Geological Science, 4(3): 10.14445/23939206/IJGGS-V4I3P102

Jega IM, Comber A, Tate NJ (2017). Spatial Planning Under Data Paucity: Dasymetric Interpolation of Population, Validated by Google Earth, to Support Health Facility Location Modelling. SSRG International Journal of Geoinformatics and Geological Science, 4(3): 10.14445/23939206/IJGGS-V4I3P101

Tsutsumida N, Harris P and Comber A(2017). The application of a geographically weighted principal components analysis for exploring 23 years of goat population change across Mongolia. Annals of the American Association of Geographers, 107(5): 1060-1074, http://dx.doi.org/10.1080/24694452.2017.1309968

Comber A, Brunsdon CF, Charlton M and Harris P (2017). Geographically weighted correspondence matrices for local change analyses and error reporting: mapping the spatial distribution of errors and change. Remote Sensing Letters, 8(3): 234-243, http://dx.doi.org/10.1080/2150704X.2016.1258126

Comber A, Harris P and Tsutsumida N (2016). Improving land cover classification using input variables derived from a geographically weighted principal components analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 119: 347–360, doi:10.1016/j.isprsjprs.2016.06.014.

Comber A, Mooney P, Purves RS,Rocchini D and WalzA (2016). Crowdsourcing: It Matters Who the Crowd Are. The Impacts of between Group Variations in Recording Land Cover. PlosONE, 11(7): e0158329, DOI: 10.1371/journal.pone.0158329

Alshwesh, I.O., Comber, A., and Brunsdon, C. (2016). GIS-Based impact of different search heuristics in relation to demand surface characteristics: comparing P-median with Grouping Genetic Algorithm approaches.Journal Of Arabic And Human Sciences, 9(3).

Comber A, Davies H, Pinder D, Whittow JB, Woodhall A and Johnson SCM (2016). Mapping coastal land use changes 1965-2014: methods for handling historical thematic data. Transactions of the Institute of British Geographers, DOI: 10.1111/tran.12128

See L, Mooney P, Foody G, Bastin L, Comber A, Estima J, Steffen F, Kerle N, Jiang B, Laakso M, Liu HY, Milinski G, Nikši M, Painho m, Pdör A, Olteanu-Raimond AM and Rutzinger M (2016). Crowdsourcing, Citizen Science or Volunteered Geographic Information? The Current State of Crowdsourced Geographic Information, ISPRS International Journal of Geo-Information, 5:55

Comber A, Balzter H, Cole B, Johnson S, Oguto B and Fisher P, (2016). Methods to quantify regional differences in land cover change. Remote Sensing,8, 176; doi:10.3390/rs8030176

Comber A, Fonte C, Foody G, Fritz S, Harris P, Raimond A-M and See L, (2016). Geographically weighted evidence combination approaches for combining discordant and inconsistent volunteered geographical information. GeoInformatica, 20(3): 503-527. Doi: 0.1007/s10707-016-0248-z

Tsutsumida N, Comber AJ, Barrett K, Saizen I, Rustiadi E (2016). Sub-pixel classification of MODIS EVI to map annual impervious surface areas. Remote Sensing, 8, 143; doi:10.3390/rs8020143

Lesiv M, Moltchanova E, Schepaschenko D, See L, Shvidenko A, Comber A and Fritz S (2016). Comparison of data fusion methods using crowdsourced data in creating a hybrid forest cover map.  Remote Sensing, 8, 261; doi:10.3390/rs8030261

Kuta, A.A., and Comber, A.J. (2015). A Fuzzy approach to modelling land cover changes in north-eastern Nigeria. International Journal of Geomatics and Geosciences, 6(2), 1620-1637.

Comber A, Dickie J, Jarvis C, Phillips M and Tansey K, (2015). Locating bioenergy facilities using a modified GIS-based location-allocation-algorithm: considering the spatial distribution of resource supply. Applied Energy, 154: 309-316. doi: 10.1016/j.apenergy.2015.04.128

Tsutsumida N. and Comber A.J. (2015). Measures of spatio-temporal accuracy for time series land cover data. International Journal of Applied Earth Observation and Geoinformation, 41: 46-55

Tewkesbury AP, Comber AJ, Tate NJ, Lamb A, Fisher PF (2015).A critical synthesis of remotely sensed optical image change detection techniques. Remote Sensing of Environment, 160:1-14doi:10.1016/j.rse.2015.01.006

Comber, A. and Brunsdon, C. (2015). A spatial analysis of plant phenophase changes and the impact of increases in urban land use. International Journal of Climatology, 35(6): 972-980. DOI: 10.1002/joc.4030

Bodicoat DH, Carter P, Comber A, Edwardson C, Gray LJ Hill S, Webb D, Yates T, Davies MJ, Khunti, K (2015). Is the number of fast food outlets in the neighbourhood related to screen-detected type 2 diabetes mellitus and associated risk factors? Public Health Nutrition, 18(09): 1698-1705. doi:10.1017/S1368980014002316

See, L, Schepaschenko, D, Lesiv, M, McCallum, I, Fritz, S, Comber, A, Perger, C, Schill, C et al (2015) Building a Hybrid Land Cover Map with Crowdsourcing and Geographically Weighted Regression. ISPRS Journal of Photogrammetry and Remote Sensing, 103: 48-56, http://dx.doi.org/10.1016/j.isprsjprs.2014.06.016

van Beijma, S, Comber, A and Lamb, A. (2014). Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data. Remote Sensing of Environment, 149:118-129

Foody, G.M., See, L., Fritz, S., Van der Velde, M., Perger, C., Schill, C., Boyd, D.S. and Comber, A., (2014). Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality. The Cartographic Journal, 52(4): 336-344 DOI: http://dx.doi.org/10.1179/1743277413Y.0000000070

Comber, A., Brunsdon, C., See, L., Fritz, S. and McCallum, I. (2013). Comparing expert and non-expert conceptualisations of the land: an analysis of crowdsourced land cover data. Lecture Notes in Computer Science: Spatial Information Theory, 8116: 243-260, doi: 10.1007/978-3-319-01790-7_14

See, L., Comber, A.J., Salk, C., Fritz, S., Van der Velde, M., Perger, C., Schill, C., McCallum, I., Kraxner, F. and Obersteiner M. (2013). Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts. PLoS ONE 8(7): e69958. doi:10.1371/journal.pone. 0069958

Zhou, R., Li, Y., Umezaki, M., Ding, Y., Jiang, H., Comber, A. and Fu, H. (2013). Association between physical activity and neighborhood environmentamong middle-age adults in Shanghai. Journal of Environmental and Public Health http://dx.doi.org/10.1155/2013/239595

Comber, A., See, L., Fritz, S., Van der Velde, M., Perger, C., Foody, G.M. (2013). Using control data to determine the reliability of volunteered geographic information about land cover. International Journal of Applied Earth Observation and Geoinformation, 23: 37–48 http://dx.doi.org/10.1016/j.jag.2012.11.002

Khalefa, E., Smit, I.P.J., Nickless, A., Archibald, S., Comber, A. and Balzter, H. (2013). Retrieval of savannah vegetation height from ICESat-GLAS speacborne LiDAR with terrain correction. IEEE Geoscience and Remote Sensing Letters, DOI: 10.1109/LGRS.2013.2259793

Comber A.J.,(2013). Geographically weighted methods for estimating local surfaces of overall, user and producer accuracies. Remote Sensing Letters, 4(4): 373-380 DOI: 10.1080/2150704X.2012.736694

Comber, A., Fisher, P.F., Brunsdon, C. and Khmag, A. (2012). Spatial analysis of remote sensing image classification accuracy. Remote Sensing of Environment, 127: 237–246.http://dx.doi.org/10.1016/j.rse.2012.09.005

Brunsdon, C. and Comber, A.J.(2012). Experiences with Citizen Science: Assessing Changes in the North American Spring. GeoInformatica,16(4): 675-690. DOI 10.1007/s10707-012-0159-6

Comber A., Brunsdon, C. and Farmer, C. (2012). Community detection in spatial networks: inferring land use from a planar graph of land cover objects. International Journal of Applied Earth Observation and Geoinformation, 8: 274–282.

Comber A., Brunsdon, C. and Phillips, M. (2012). The varying impact of geographic distance as a predictor of dissatisfaction over facility access. Applied Spatial Analysis and Policy, 5: 333–352, DOI: 10.1007/s12061-011-9074-8

Carver S, Comber A,McMorran R and Nutter S. (2012). A GIS model for mapping spatial patterns and distribution of wild land in Scotland. Landscape and Urban Planning, 104 (2012) 395–409.

Comber, A., Umezaki, M., Zhou, R., Ding, Y., Li, Y., Fu, H, Jiang, H. and Tewkesbury, A. (2012). Using shadow in high resolution imagery to map residential density. Remote Sensing Letters, 3:7, 551-556

Comber A., Brunsdon, C. and Radburn, R. (2011). A spatial analysis of variations in health access: linking geography, socio-economic status and access perceptions. International Journal of Health Geographics, 10:44

Comber, A.J.,Sasaki, S., Suzuki, H. and Brunsdon, C., (2011). A modified grouping genetic algorithm to select ambulance site locations. International Journal of Geographical Information Science, 25(5): 807–823 doi:10.1080/13658816.2010.501334

Elaalem, M., Comber, A., and Fisher, P., (2011). Land Evaluation Techniques ComparingFuzzy AHP with Ideal Point methods. Transactions in GIS, 15(3): 329–346 doi: 10.1111/j.1467-9671.2011.01260.x

Sasaki, S., Igarashi, K., Fujino, Y., Comber, A.J. and Brunsdon, C., Muleya, C.M. and Suzuki, H. (2011). The impact of community-based outreach immunization services on immunization coverage with GIS network accessibility analysis in peri-urban areas, Zambia. Journal of Epidemiology and Community Health,65: 1171-1178 doi:10.1136/jech.2009.104190

Comber, A.J.,Carver, S., Fritz, S., McMorran, R., Washtell, J. and Fisher, P. (2010). Different methods, different wilds: evaluating alternative mappings of wildness using Fuzzy MCE and Dempster Shafer MCE. Computers, Environment and Urban Systems, 34: 142-152

Comber, A., Medcalf, K., Lucas, R., Bunting, Brown, A., P Clewley, D., Breyer, J. and Keyworth, S., (2010).Managing uncertainty when aggregating from pixels to objects: context sensitive mapping and possibility theory. International Journal of Remote Sensing, 31(4): 1061-1068.

Sasaki, S., Comber, A.J., Suzuki, H. and Brunsdon, C., (2010). Using genetic algorithms to optimise current and future health planning - the example of ambulance locations. International Journal of Health Geographics, 9: 4. doi:10.1186/1476-072X-9-4

Comber, A.J., Brunsdon, C., Hardy, J. and Radburn, R. (2009). Using a GIS–based network analysis and optimisation routines to evaluate service provision: a case study of the UK Post Office Applied Spatial Analysis and Policy, 2(1):47-64

Kemawy, A.H. and Comber, A.J., (2009). Modeling spatial variability of water quality: a case study of Lake Manzala. Bulletin of the Egyptian Geographical Society, 82:33-51

Comber, A.J.,Brunsdon, C. and Green, E. (2008). Using a GIS-based network analysis to determine urban greenspace accessibility for different ethnic and religious groups. Landscape and Urban Planning, 86: 103–114.

Comber, A.J.,Proctor, C. and Anthony, S. (2008). The creation of a national agricultural land use dataset: combining pycnophylactic interpolation with dasymetric mapping techniquesTransactions in GIS, 12(6): 775–791.

Comber, A.J., (2008). Land Cover or Land Use?, Editorial, Journal of Land Use Science, 3(4): 199–201.

Comber, A., (2008). The separation of land cover from land use with data primitives. Journal of Land Use Science, 3(4): 215–229

Wadsworth, R., Balzter, H., Gerard, F., George, C., Comber, A.and Fisher, P. (2008). An environmental assessment of land cover and land use change in Central Siberia using Quantified Conceptual Overlaps to reconcile inconsistent data sets. Journal of Land use Science, 3(4): 251–264.

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2008). Using semantics to clarify the conceptual confusion between land cover and land use: the example of ‘forest’. Journal of Land use Science, 3(2-3): 185-198

Comber, A.J., Fisher, P.F. and Wadsworth, R.A., (2008). Semantics, Metadata, Geographical Information and Users. Editorial Transactions in GIS, 12(3): 287–291

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2007). Land cover: to standardise or not to standardise? Comment on ‘Evolving standards in land cover characterization’ by Herold et al. Journal of Land Use Science, 2(4): 287–291

Procter, C., Comber, L.,Betson, M., Buckley, D.,Frost, A., Lyons, H., Riding, A., and Voyce, K. (2006) Identifying crop vulnerability to groundwater abstraction: modelling and expert knowledge in a GIS. Journal of Environmental Management, 81: 296 - 306

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2005). A comparison of statistical and expert approaches to data integration. Journal of Environmental Management, 77: 47-55.

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2005). Comparing the consistency of expert land cover knowledge. International Journal of Applied Earth Observation and Geoinformation, 7(3): 189-201.

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2005). What is land cover? Environment and Planning B: Planning and Design, 32:199-209.

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2005). You know what land cover is but does anyone else?…an investigation into semantic and ontological confusion. International Journal of Remote Sensing, 26 (1): 223-228

Comber, A.J.,Fisher, P.F., Wadsworth, R.A., (2004). Assessment of a Semantic Statistical Approach to Detecting Land Cover Change Using Inconsistent Data Sets. Photogrammetric Engineering and Remote Sensing, 70(8): 931-938.

Comber, A.,Fisher, P., Wadsworth, R., (2004). Integrating land cover data with different ontologies: identifying change from inconsistency. International Journal of Geographical Information Science, 18(7): 691-708.

Comber, A.J.,Law, A.N.R., Lishman, J.R., (2004). A comparison of Bayes', Dempster-Shafter and endorsement theories for managing knowledge uncertainty in the context of land cover monitoring, Computers, Environment and Urban Systems, 28(4): 311-327

Comber, A.J.,Law, A.N.R., Lishman, J.R., (2004). Application of knowledge for automated land cover change monitoring. International Journal of Remote Sensing, 25(16): 3177-3192.

Comber, A.,Fisher, P., Wadsworth, R., (2003) Actor Network Theory: a suitable framework to understand how land cover mapping projects develop? Land Use Policy, 20: 299–309.

Comber, A.J.,Birnie, R.V., Hodgson, M. (2003). A retrospective analysis of land cover change using polygon shape index. Global Ecology and Biogeography, 12: 207-215.

Peer Reviewed Computer Science Proceedings

Jeansoulin, R., Pham, T.T. and Comber, A.J. (2004). Land cover change detection: a quality-aware and semantic-based approach. In Proceedings of Information Processing and Management of Uncertainty in Knowledge-Based Systems 2004, (eds. B. Bouchon-Meunier, G. Coletti, and R. R. Yager), 4th-9th July, Universita' di Perugia, Perugia.

<h4>Research projects</h4> <p>Any research projects I'm currently working on will be listed below. Our list of all <a href="https://environment.leeds.ac.uk/dir/research-projects">research projects</a> allows you to view and search the full list of projects in the faculty.</p>

Current postgraduate research students

<h4>Postgraduate research opportunities</h4> <p>We welcome enquiries from motivated and qualified applicants from all around the world who are interested in PhD study. Our <a href="https://environment.leeds.ac.uk/research-opportunities">research opportunities</a> allow you to search for projects and scholarships.</p>