GEP  Vol.4 No.7 , July 2016
Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery
Abstract: The aim of this project is to create high resolution land cover classification as well as tree canopy density maps at a regional level using high resolution spatial data. Modeling and the data manipulation and analysis of LiDAR LAS point cloud dataset as well as multispectral aerial photographs from the National Agriculture Imagery Program (NAIP) were carried out. Using geoprocessing modeling, a land cover map is created based on filtered returns from LiDAR point cloud data (LAS dataset) to extract features based on their class and return values, and traditional classification methods of high resolution multi-spectral aerial photographs of the remaining ground cover for Clarion County in Pennsylvania. The newly developed model produced 7 classes at 10 ft × 10 ft spatial resolution, namely: water bodies, structures, streets and paved surfaces, bare ground, grassland, trees, and artificial surfaces (e.g. turf). The model was tested against areas with different sizes (townships and municipalities) which revealed a classification accuracy between 94% and 96%. A visual observation of the results shows that some tree-covered areas were misclassified as built up/structures due to the nature of the available LiDAR data, an area of improvement for further studies. Furthermore, a geoprocessing service was created in order to disseminate the results of the land cover classification as well as the tree canopy density calculation to a broader audience. The service was tested and delivered in the form of a web application where users can select an area of interest and the model produces the land cover and/or the tree canopy density results ( The produced output can be printed as a final map layout with the highlighted area of interest and its corresponding legend. The interface also allows the download of the results of an area of interest for further investigation and/or analysis.
Cite this paper: Ayad, Y. (2016) Developing an Automated Land Cover Classifier Using LiDAR and High Resolution Aerial Imagery. Journal of Geoscience and Environment Protection, 4, 97-110. doi: 10.4236/gep.2016.47011.

[1]   Chrysoulakis, N., Feigenwinter, C., Triantakonstantis, D., Penyevskiy, I., Tal, A., Parlow, E., Fleishman, G., Düzgün, S., Esch, T. and Marconcini, M. (2014) A Conceptual List of Indicators for Urban Planning and Management Based on Earth Observation. ISPRS International Journal of Geo-Information, 3, 980-1002.

[2]   Li, E., Du, P., Samat, A., Xia, J. and Che, M. (2015) An Automatic Approach for Urban Land-Cover Classification from Landsat-8 OLI Data. International Journal of Remote Sensing, 36, 5983-6007.

[3]   Haberman, D., Gillies, L., Canter, A., Rinner, V., Pancrazi, L. and Martellozzo, F. (2014) The Potential of Urban Agriculture in Montréal: A Quantitative Assessment. ISPRS International Journal of Geo-Information, 3, 1101-1117.

[4]   Reimer, M. (2013) Planning Cultures in Transition: Sustainability Management and Institutional Change in Spatial Planning. Sustainability, 5, 4653-4673.

[5]   Wächter, P. (2013) The Impacts of Spatial Planning on Degrowth. Sustainability, 5, 1067-1079.

[6]   Ding, C., Wang, Y., Xie, B. and Liu, C. (2014) Understanding the Role of Built Environment in Reducing Vehicle Miles Traveled Accounting for Spatial Heterogeneity. Sustainability, 6, 589-601.

[7]   Piragnolo, M., Pirotti, F., Guarnieri, A., Vettore, A. and Salogni, G. (2014) Geo-Spatial Support for Assessment of Anthropic Impact on Biodiversity. ISPRS International Journal of Geo-Information, 3, 599-618.

[8]   Florin, M., Murariu, G., Ionut, M. and Georgescu, L.P. (2011) Environment Monitoring through UAV Technologies. Analele Universitatii “Dunarea de Jos” Galati. Fascicle II: Mathematics, Physics, Theoretical Mechanics, 34, 300-308.

[9]   Lederwasch, A. and Mukheibir, P. (2013) The Triple Bottom Line and Progress toward Ecological Sustainable Development: Australia’s Coal Mining Industry as a Case Study. Resources, 2, 26-38.

[10]   Lei, L. and Hilton, B. (2013) A Spatially Intelligent Public Participation System for the Environmental Impact Assessment Process. ISPRS International Journal of Geo-Information, 2, 480-506.

[11]   Liang, X., Hyyppä, J., Kaartinen, H., Holopainen, M. and Melkas, T. (2012) Detecting Changes in Forest Structure over Time with Bi-Temporal Terrestrial Laser Scanning Data. ISPRS International Journal of Geo-Information, 1, 242-255.

[12]   Ahmed, B. and Ahmed, R. (2012) Modeling Urban Land Cover Growth Dynamics Using Multi-Temporal Satellite Images: A Case Study of Dhaka, Bangladesh. ISPRS International Journal of Geo-Information, 1, 3-31.

[13]   Yeshaneh, E., Wagner, W., Exner-Kittridge, M., Legesse, D. and Blöschl, G. (2013) Identifying Land Use/Cover Dynamics in the Koga Catchment, Ethiopia, from Multi-Scale Data, and Implications for Environmental Change. ISPRS International Journal of Geo-Information, 2, 302-323.

[14]   Forsythe, K.W. and McCartney, G. (2014) Investigating Forest Disturbance Using Landsat Data in the Nagagamisis Central Plateau, Ontario, Canada. ISPRS International Journal of Geo-Information, 3, 254-273.

[15]   Forsythe, K.W., Schatz, B., Swales, S.J., Ferrato, L.-J. and Atkinson, D.M. (2012) Visualization of Lake Mead Surface Area Changes from 1972 to 2009. ISPRS International Journal of Geo-Information, 1, 108-119.

[16]   Tran, H., Tran, T. and Kervyn, M. (2015) Dynamics of Land Cover/Land Use Changes in the Mekong Delta, 1973-2011: A Remote Sensing Analysis of the Tran Van Thoi District, Ca Mau Province, Vietnam. Remote Sensing, 7, 2899-2925.

[17]   Setiawan, Y., Rustiadi, E., Yoshino, K., Liyantono and Effendi, H. (2014) Assessing the Seasonal Dynamics of the Java’s Paddy Field Using MODIS Satellite Images. ISPRS International Journal of Geo-Information, 3, 110-129.

[18]   Prilepova, O., Hart, Q., Merz, J., Parker, N., Bandaru, V. and Jenkins, B. (2014) Design of a GIS-Based Web Application for Simulating Biofuel Feedstock Yields. ISPRS International Journal of Geo-Information, 3, 929-941.

[19]   Yen, H., Sharifi, A., Kalin, L., Mirhosseini, G. and Arnold, J.G. (2015) Assessment of Model Predictions and Parameter Transferability by Alternative Land Use Data on Watershed Modeling. Journal of Hydrology, 527, 458-470.

[20]   Ongsomwang, S. and Pimjai, M. (2015) Land Use and Land Cover Prediction and Its Impact on Surface Runoff. Suranaree Journal of Science and Technology, 22, 205-223.

[21]   Halmy, M.W.A., Gessler, P.E., Hicke, J.A. and Salem, B.B. (2015) Land Use/Land Cover Change Detection and Prediction in the North-Western Coastal Desert of Egypt Using Markov-CA. Applied Geography, 63, 101-112.

[22]   Machala, M., Honzová, M. and Klimánek, M. (2015) Generating Land-Cover Maps from Remotely Sensed Data: Manual Vectorization versus Object-Oriented Automation.

[23]   Sun, L. and Schulz, K. (2015) The Improvement of Land Cover Classification by Thermal Remote Sensing. Remote Sensing, 7, 8368-8390.

[24]   Babamaaji, R. and Lee, J. (2014) Land Use/Land Cover Classification of the Vicinity of Lake Chad Using NigeriaSat-1 and Landsat Data. Environmental Earth Sciences, 71, 4309-4317.

[25]   Jing, L. and Yi, L. (2015) Hyperspectral Remote Sensing Images Terrain Classification in DCT SRDA Subspace. The Journal of China Universities of Posts and Telecommunications, 22, 65-71.

[26]   Salah, M., Trinder, J.C. and Shaker, A. (2011) Performance Evaluation of Classification Trees for Building Detection from Aerial Images and LiDAR Data: A Comparison of Classification Trees Models. International Journal of Remote Sensing, 32, 5757-5783.

[27]   Panda, S.S., Hoogenboom, G. and Paz, J.O. (2010) Remote Sensing and Geospatial Technological Applications for Site-Specific Management of Fruit and Nut Crops: A Review. Remote Sensing, 2, 1973-1997.

[28]   Gu, Y., Wang, Q., Jia, X. and Benediktsson, J.A. (2015) A Novel MKL Model of Integrating LiDAR Data and MSI for Urban Area Classification. IEEE Transactions on Geoscience and Remote Sensing, 53, 5312-5326.

[29]   Díaz-Vilariño, L., González-Jorge, H., Bueno, M., Arias, P. and Puente, I. (2016) Automatic Classification of Urban Pavements Using Mobile LiDAR Data and Roughness Descriptors. Construction and Building Materials, 102, 208-215.

[30]   Arroyo, L.A., Phinn, S., Armston, J. and Johansen, K. (2010) Integration of LiDAR and QuickBird Imagery for Mapping Riparian Biophysical Parameters and Land Cover Types in Australian Tropical Savannas [Electronic Resource]. Forest Ecology and Management, 259, 598-606.

[31]   Lin, Y. and Hyyppä, J. (2016) A Comprehensive but Efficient Framework of Proposing and Validating Feature Parameters from Airborne LiDAR Data for Tree Species Classification. International Journal of Applied Earth Observation and Geoinformation, 46, 45-55.

[32]   Schumacher, J. and Nord-Larsen, T. (2014) Wall-to-Wall Tree Type Classification Using Airborne Lidar Data and CIR Images. International Journal of Remote Sensing, 35, 3057-3073.

[33]   Rapinel, S., Hubert-Moy, L. and Clément, B. (2015) Combined Use of LiDAR Data and Multispectral Earth Observation Imagery for Wetland Habitat Mapping. International Journal of Applied Earth Observation and Geoinformation, 37, 56-64.

[34]   Sinagra, O. and Lim, S. (2014) Data Fusion and Supervised Classifications with Lidar Data and Multispectral Imagery. Proceedings of the 14th International Multidisciplinary Scientific GeoConference SGEM, 3, 129-136.

[35]   NAIP Imagery. [Online].

[36]   PASDA—The Pennsylvania Spatial Data Clearinghouse. [Online].

[37]   Luo, S.Z., Wang, C., Xi, X.H., Zeng, H.C., Li, D., Xia, S.B. and Wang, P.H. (2016) Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification. Remote Sensing, 8, 1-19.