Research Article
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Year 2019, Volume: 7 Issue: 2, 166 - 186, 20.08.2019
https://doi.org/10.31195/ejejfs.597460

Abstract

References

  • Akima H. (1978). A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Transactions on Mathematical Software (TOMS) 4:(2) 148-159. https://doi.org/10.1145/355780.355786.
  • Ardiansyah P. O. D., Yokoyama R. (2002). DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function. ISPRS Journal of Photogrammetry and Remote Sensing 57:(1) 86-101. https://doi.org/10.1016/S0924-2716(02)00117-X.
  • Aruga K., Chung W., Akay A., Sessions J., Miyata E. S. (2007). Incorporating Soil Surface Erosion Prediction into Forest Road Alignment Optimization. International Journal of Forest Engineering 18:(1) 24-32. 10.1080/14942119.2007.10702541.
  • Aryal R. R., Latifi H., Heurich M., Hahn M. (2017). Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 85:(4) 243-255. 10.1007/s41064-017-0023-2.
  • Aydın A., Tecimen H. B. (2010). Temporal soil erosion risk evaluation: a CORINE methodology application at Elmalı dam watershed, Istanbul. Environmental Earth Sciences 61:(7) 1457-1465. 10.1007/s12665-010-0461-2.
  • Band L. E. (1986). Topographic partition of watersheds with digital elevation models. Water Resources Research 22:(1) 15-24
  • Bater C. W., Coops N. C. (2009). Evaluating error associated with lidar-derived DEM interpolation. Computers & Geosciences 35:(2) 289-300. https://doi.org/10.1016/j.cageo.2008.09.001.
  • Behrens T., Schmidt K., MacMillan R. A., Viscarra Rossel R. A. (2018). Multi-scale digital soil mapping with deep learning. Scientific Reports 8:(1) 15244. 10.1038/s41598-018-33516-6.
  • Behrens T., Zhu A. X., Schmidt K., Scholten T. (2010). Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155:(3) 175-185. https://doi.org/10.1016/j.geoderma.2009.07.010.
  • Bettinger P., Boston K., Siry J. P., Grebner D. L. (2010). Forest Management and PlanningElsevier Science.
  • Beven K. J., Kirkby M. J. (1979). A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Bulletin 24:(1) 43-69. 10.1080/02626667909491834.
  • Blöschl G., Sivapalan M. (1995). Scale issues in hydrological modelling: A review. Hydrological Processes 9:(3‐4) 251-290. 10.1002/hyp.3360090305.
  • Briggs I. C. (1974). Machine contouring using minimum curvature. Geophysics 39:(1) 39-48. https://doi.org/10.1190/1.1440410.
  • Burrough P. A. (1986). Principles of geographical information systems for land resources assessment. Monographs on soil and resources survey. Clarendon Press, Oxford University Press Oxford Oxfordshire, New York.
  • Chang K.-t., Tsai B.-w. (1991). The Effect of DEM Resolution on Slope and Aspect Mapping. Cartography and Geographic Information Systems 18:(1) 69-77. 10.1559/152304091783805626.
  • Chen Y., Zhou Q. (2013). A scale-adaptive DEM for multi-scale terrain analysis. International Journal of Geographical Information Science 27:(7) 1329-1348. 10.1080/13658816.2012.739690.
  • De Meij A., Bossioli E., Penard C., Vinuesa J. F., Price I. (2015). The effect of SRTM and Corine Land Cover data on calculated gas and PM10 concentrations in WRF-Chem. Atmospheric Environment 101: 177-193. https://doi.org/10.1016/j.atmosenv.2014.11.033.
  • Dong P., Chen Q. (2017). LiDAR Remote Sensing and ApplicationsCRC Press Boca Raton.Dragos B., Karsten B. J. (2008). Filtering Process of LIDAR Data. XXIst ISPRS Congress, Beijing, CHINA.
  • Duvemo K., Lämås T. (2006). The influence of forest data quality on planning processes in forestry. Scandinavian Journal of Forest Research 21:(4) 327-339. 10.1080/02827580600761645.
  • Evans I. S. (1980). An integrated system of terrain analysis and slope mapping. Zeitschrift fur Geomorphologie, Supplementband 36: 274-295
  • Fernández-Landa A., Fernández-Moya J., Tomé J. L., Algeet-Abarquero N., Guillén-Climent M. L., Vallejo R., Sandoval V., Marchamalo M. (2018). High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar. International Journal of Remote Sensing 39:(14) 4830-4844. 10.1080/01431161.2018.1430406.
  • Fisher P. F., Tate N. J. (2006). Causes and consequences of error in digital elevation models. Progress in Physical Geography 30:(4) 467-489
  • Fleming C., Giles J. R. A., Marsh S. H. (2010). Elevation Models for GeoscienceGeological Society.
  • Flores-Prieto E., Quénéhervé G., Bachofer F., Shahzad F., Maerker M. (2015). Morphotectonic interpretation of the Makuyuni catchment in Northern Tanzania using DEM and SAR data. Geomorphology 248: 427-439. https://doi.org/10.1016/j.geomorph.2015.07.049.
  • Florinsky I. (2016). Digital Terrain Analysis in Soil Science and GeologyElsevier Science.
  • Fortune S. (1987). A sweepline algorithm for Voronoi diagrams. Algorithmica 2:(1-4) 153-174. https://doi.org/10.1007/BF01840357.
  • General Directorate of Mapping. (2019). Technical Characteristics of Products, 1/25000 Scale Topographical Map [online]. Available at: https://www.harita.gov.tr/english/u-1-maps.html [Accessed 5th June 2019].
  • Gonga-Saholiariliva N., Gunnell Y., Petit C., Mering C. (2011). Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis. Progress in Physical Geography: Earth and Environment 35:(6) 739-764. 10.1177/0309133311409086.
  • Goodbody R. T., Coops C. N., Hermosilla T., Tompalski P., Pelletier G. (2018). Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models. Remote Sensing 10:(10). 10.3390/rs10101554.
  • Goodchild M. F., Mark D. M. (1987). The fractal nature of geographic phenomena. Annals of the Association of American Geographers 77:(2) 265-278
  • Grohmann C. H. (2015). Effects of spatial resolution on slope and aspect derivation for regional-scale analysis. Computers & Geosciences 77: 111-117. https://doi.org/10.1016/j.cageo.2015.02.003.
  • Hardy R. L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of geophysical research 76:(8) 1905-1915. https://doi.org/10.1029/JB076i008p01905 Hengl T. (2006). Finding the right pixel size. Computers & Geosciences 32:(9) 1283-1298. https://doi.org/10.1016/j.cageo.2005.11.008.
  • Hutchinson M. (1989). A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106:(3-4) 211-232. https://doi.org/10.1016/0022-1694(89)90073-5.
  • Hutchinson M. (1996). A locally adaptive approach to the interpolation of digital elevation models. Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling.
  • Hutchinson M., Bischof R. (1983). A new method for estimating the spatial distribution of mean seasonal and annual rainfall applied to the Hunter Valley, New South Wales. Australian Meteorological Magazine 31:(3) 179-184
  • Hutchinson M. F., Xu T., Stein J. A. (2011). Recent progress in the ANUDEM elevation gridding procedure. Proceedings of the Geomorphometry 19-22
  • Jing C., Shortridge A., Lin S., Wu J. (2014). Comparison and validation of SRTM and ASTER GDEM for a subtropical landscape in Southeastern China. International Journal of Digital Earth 7:(12) 969-992. 10.1080/17538947.2013.807307.
  • Li X., Shen H., Feng R., Li J., Zhang L. (2017). DEM generation from contours and a low-resolution DEM. ISPRS Journal of Photogrammetry and Remote Sensing 134: 135-147. https://doi.org/10.1016/j.isprsjprs.2017.09.014.
  • Li Z., Zhu Q., Gold C. (2005). Digital terrain modeling : principles and methodologyCRC Press New York.
  • Lidberg W., Nilsson M., Ågren A. (2019). Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio. 10.1007/s13280-019-01196-9.
  • Liu X. (2008). Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography 32:(1) 31-49. 10.1177/0309133308089496.
  • Makarovic B. (1977). Regressive rejection—a digital data compression technique. Proc. ASP/ACSM Fall Technical Meeting, Little Rock, USA.
  • Makarovic B. (1984). Structures for geo-information and their application in selective sampling for digital terrain models. ITC journal(4) 285-295Mitas L., Mitasova H. (1999). Spatial interpolation. In P. Longley et al. eds. Geographical information systems: principles, techniques, management and applications. New York, Wiley. 481-492.
  • Mo D., Fuchs H., Fehrmann L., Yang H., Lu Y., Kleinn C. (2015). Local Parameter Estimation of Topographic Normalization for Forest Type Classification. IEEE Geoscience and Remote Sensing Letters 12:(9) 1998-2002. 10.1109/LGRS.2015.2448937.
  • Mukherjee S., Joshi P. K., Mukherjee S., Ghosh A., Garg R. D., Mukhopadhyay A. (2013). Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation 21: 205-217. https://doi.org/10.1016/j.jag.2012.09.004.
  • Nyquist H. (1924). Certain Factors Affecting Telegraph Speed. Transactions of the American Institute of Electrical Engineers XLIII: 412-422. 10.1109/T-AIEE.1924.5060996.
  • Polat N., Uysal M. (2015). Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation. Measurement 63: 61-68. https://doi.org/10.1016/j.measurement.2014.12.017.
  • Rather M. A., Farooq M., Meraj G., Dada M. A., Sheikh B. A., Wani I. A. (2018). Remote Sensing and GIS Based Forest Fire Vulnerability Assessment in Dachigam National Park, North Western Himalaya. Asian Journal of Applied Sciences 11: 98-114
  • Rexer M., Hirt C. (2014). Comparison of free high resolution digital elevation data sets (ASTER GDEM2, SRTM v2.1/v4.1) and validation against accurate heights from the Australian National Gravity Database. Australian Journal of Earth Sciences 61:(2) 213-226. 10.1080/08120099.2014.884983.
  • Robinson A. H., Morrison J. L., Muehrcke P. C., Kimerling A. J., Guptill S. C. (2009). Elements of Cartography. 6th edWiley India Pvt. Limited.
  • Shannon C. E. (1934). Communication in the presence of noise. Proc. Inst. Radio Eng 371.
  • Szabó G., Singh S. K., Szabó S. (2015). Slope angle and aspect as influencing factors on the accuracy of the SRTM and the ASTER GDEM databases. Physics and Chemistry of the Earth, Parts A/B/C 83-84: 137-145. https://doi.org/10.1016/j.pce.2015.06.003.
  • Tachikawa T., Kaku M., Iwasaki A., Gesch D. B., Oimoen M. J., Zhang Z., Danielson J. J., Krieger T., Curtis B., Haase J., Abrams M., Carabajal C. (2011). ASTER Global Digital Elevation Model Version 2 - summary of validation results. 27.
  • Talhofer V., Kovarik V., Rybansky M., Hofmann A., Hubacek M., Hoskova-Mayerova S. (2015). Terrain Analysis for Armed Forces. In J. Brus, A. Vondrakova, V. Vozenilek eds. Modern Trends in Cartography: Selected Papers of CARTOCON 2014. Cham, Springer International Publishing. 519-532.
  • Tarboton D. G. (2003). Terrain analysis using digital elevation models in hydrology. 23rd ESRI international users conference, San Diego, California.
  • Tarolli P., Cavalli M., Masin R. (2019). High-resolution morphologic characterization of conservation agriculture. Catena 172: 846-856. https://doi.org/10.1016/j.catena.2018.08.026.
  • Toutin T. (2002). Impact of terrain slope and aspect on radargrammetric DEM accuracy. ISPRS Journal of Photogrammetry and Remote Sensing 57:(3) 228-240. https://doi.org/10.1016/S0924-2716(02)00123-5.
  • Watson D. F. (1992). Contouring: A Guide to the Analysis and Display of Spatial DataPergamon Press.
  • Wilson J. P., Gallant J. C. (2000). Terrain analysis : principles and applicationsWiley New York.
  • Wong W. V. C., Tsuyuki S., Loki K., Phua M. H. (2014). Accuracy assessment of global topographic data (SRTM & ASTER GDEM) in comparison with lidar for tropical montane forest. Proceedings of the 35th Asian Conference on Remote Sensing, Nay Pyi Taw, Myanmar.
  • Yurtseven H. (2019). Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on The Basis of Spatial Differences. ISPRS International Journal of Geo-Information 8:(4). 10.3390/ijgi8040175.
  • Yurtseven H., Akgul M., Coban S., Gulci S. (2019). Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques. Measurement 145: 651-664. https://doi.org/10.1016/j.measurement.2019.05.092.
  • Zhang X., Drake N. A., Wainwright J., Mulligan M. (1999). Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution. Earth Surface Processes and Landforms 24:(9) 763-779. 10.1002/(SICI)1096-9837(199908)24:9<763::AID-ESP9>3.0.CO;2-J.

Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area

Year 2019, Volume: 7 Issue: 2, 166 - 186, 20.08.2019
https://doi.org/10.31195/ejejfs.597460

Abstract

DEMs (Digital Elevation Model) generated with different remote sensing techniques and technologies are used to determine the changes of vegetation in forests depending on topographical factors. The accuracy of DEMs has a major impact on the planning and management of forests.

In this study, the accuracy of two different DEM data sources, which are frequently used in the modeling of topographic changes in large field studies in forestry, was compared with the LiDAR-based DEM dataset on a forest site. In this context, three different DEM source were used. One of them was the 10 m interval contour lines of 1:25,000 scale aerial photogrammetry based standard topographical maps which are produced by National General Directorate of Mapping. Topomap contour lines are transformed to grid based DEMs by using TIN and ANUDEM based approaches at 2.5, 5, 10 and 30 m resolutions. The other data was ASTER GDEM (1 arc-second ASTER GDEM Version 2, approximately 30 m resolution). The final and reference data is the LiDAR based, 0.25 m resolution DEM. In total, 33 DEM datasets are compared with the LiDAR-based DEM dataset. For these data sets, five difference metrics were calculated: pixel based difference models, the areal and volumetric difference of surface models, the areal difference of slope classes and the areal difference of aspect classes. According to the results of the analysis, the resolution, according to the topographic characteristics of the area and selected interpolation approaches has an effect on DEM modeling and DEM –derived metrics. In addition, the forest structure has a major impact on the accuracy of ASTER GDEM data.



References

  • Akima H. (1978). A method of bivariate interpolation and smooth surface fitting for irregularly distributed data points. ACM Transactions on Mathematical Software (TOMS) 4:(2) 148-159. https://doi.org/10.1145/355780.355786.
  • Ardiansyah P. O. D., Yokoyama R. (2002). DEM generation method from contour lines based on the steepest slope segment chain and a monotone interpolation function. ISPRS Journal of Photogrammetry and Remote Sensing 57:(1) 86-101. https://doi.org/10.1016/S0924-2716(02)00117-X.
  • Aruga K., Chung W., Akay A., Sessions J., Miyata E. S. (2007). Incorporating Soil Surface Erosion Prediction into Forest Road Alignment Optimization. International Journal of Forest Engineering 18:(1) 24-32. 10.1080/14942119.2007.10702541.
  • Aryal R. R., Latifi H., Heurich M., Hahn M. (2017). Impact of Slope, Aspect, and Habitat-Type on LiDAR-Derived Digital Terrain Models in a Near Natural, Heterogeneous Temperate Forest. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science 85:(4) 243-255. 10.1007/s41064-017-0023-2.
  • Aydın A., Tecimen H. B. (2010). Temporal soil erosion risk evaluation: a CORINE methodology application at Elmalı dam watershed, Istanbul. Environmental Earth Sciences 61:(7) 1457-1465. 10.1007/s12665-010-0461-2.
  • Band L. E. (1986). Topographic partition of watersheds with digital elevation models. Water Resources Research 22:(1) 15-24
  • Bater C. W., Coops N. C. (2009). Evaluating error associated with lidar-derived DEM interpolation. Computers & Geosciences 35:(2) 289-300. https://doi.org/10.1016/j.cageo.2008.09.001.
  • Behrens T., Schmidt K., MacMillan R. A., Viscarra Rossel R. A. (2018). Multi-scale digital soil mapping with deep learning. Scientific Reports 8:(1) 15244. 10.1038/s41598-018-33516-6.
  • Behrens T., Zhu A. X., Schmidt K., Scholten T. (2010). Multi-scale digital terrain analysis and feature selection for digital soil mapping. Geoderma 155:(3) 175-185. https://doi.org/10.1016/j.geoderma.2009.07.010.
  • Bettinger P., Boston K., Siry J. P., Grebner D. L. (2010). Forest Management and PlanningElsevier Science.
  • Beven K. J., Kirkby M. J. (1979). A physically based, variable contributing area model of basin hydrology / Un modèle à base physique de zone d'appel variable de l'hydrologie du bassin versant. Hydrological Sciences Bulletin 24:(1) 43-69. 10.1080/02626667909491834.
  • Blöschl G., Sivapalan M. (1995). Scale issues in hydrological modelling: A review. Hydrological Processes 9:(3‐4) 251-290. 10.1002/hyp.3360090305.
  • Briggs I. C. (1974). Machine contouring using minimum curvature. Geophysics 39:(1) 39-48. https://doi.org/10.1190/1.1440410.
  • Burrough P. A. (1986). Principles of geographical information systems for land resources assessment. Monographs on soil and resources survey. Clarendon Press, Oxford University Press Oxford Oxfordshire, New York.
  • Chang K.-t., Tsai B.-w. (1991). The Effect of DEM Resolution on Slope and Aspect Mapping. Cartography and Geographic Information Systems 18:(1) 69-77. 10.1559/152304091783805626.
  • Chen Y., Zhou Q. (2013). A scale-adaptive DEM for multi-scale terrain analysis. International Journal of Geographical Information Science 27:(7) 1329-1348. 10.1080/13658816.2012.739690.
  • De Meij A., Bossioli E., Penard C., Vinuesa J. F., Price I. (2015). The effect of SRTM and Corine Land Cover data on calculated gas and PM10 concentrations in WRF-Chem. Atmospheric Environment 101: 177-193. https://doi.org/10.1016/j.atmosenv.2014.11.033.
  • Dong P., Chen Q. (2017). LiDAR Remote Sensing and ApplicationsCRC Press Boca Raton.Dragos B., Karsten B. J. (2008). Filtering Process of LIDAR Data. XXIst ISPRS Congress, Beijing, CHINA.
  • Duvemo K., Lämås T. (2006). The influence of forest data quality on planning processes in forestry. Scandinavian Journal of Forest Research 21:(4) 327-339. 10.1080/02827580600761645.
  • Evans I. S. (1980). An integrated system of terrain analysis and slope mapping. Zeitschrift fur Geomorphologie, Supplementband 36: 274-295
  • Fernández-Landa A., Fernández-Moya J., Tomé J. L., Algeet-Abarquero N., Guillén-Climent M. L., Vallejo R., Sandoval V., Marchamalo M. (2018). High resolution forest inventory of pure and mixed stands at regional level combining National Forest Inventory field plots, Landsat, and low density lidar. International Journal of Remote Sensing 39:(14) 4830-4844. 10.1080/01431161.2018.1430406.
  • Fisher P. F., Tate N. J. (2006). Causes and consequences of error in digital elevation models. Progress in Physical Geography 30:(4) 467-489
  • Fleming C., Giles J. R. A., Marsh S. H. (2010). Elevation Models for GeoscienceGeological Society.
  • Flores-Prieto E., Quénéhervé G., Bachofer F., Shahzad F., Maerker M. (2015). Morphotectonic interpretation of the Makuyuni catchment in Northern Tanzania using DEM and SAR data. Geomorphology 248: 427-439. https://doi.org/10.1016/j.geomorph.2015.07.049.
  • Florinsky I. (2016). Digital Terrain Analysis in Soil Science and GeologyElsevier Science.
  • Fortune S. (1987). A sweepline algorithm for Voronoi diagrams. Algorithmica 2:(1-4) 153-174. https://doi.org/10.1007/BF01840357.
  • General Directorate of Mapping. (2019). Technical Characteristics of Products, 1/25000 Scale Topographical Map [online]. Available at: https://www.harita.gov.tr/english/u-1-maps.html [Accessed 5th June 2019].
  • Gonga-Saholiariliva N., Gunnell Y., Petit C., Mering C. (2011). Techniques for quantifying the accuracy of gridded elevation models and for mapping uncertainty in digital terrain analysis. Progress in Physical Geography: Earth and Environment 35:(6) 739-764. 10.1177/0309133311409086.
  • Goodbody R. T., Coops C. N., Hermosilla T., Tompalski P., Pelletier G. (2018). Vegetation Phenology Driving Error Variation in Digital Aerial Photogrammetrically Derived Terrain Models. Remote Sensing 10:(10). 10.3390/rs10101554.
  • Goodchild M. F., Mark D. M. (1987). The fractal nature of geographic phenomena. Annals of the Association of American Geographers 77:(2) 265-278
  • Grohmann C. H. (2015). Effects of spatial resolution on slope and aspect derivation for regional-scale analysis. Computers & Geosciences 77: 111-117. https://doi.org/10.1016/j.cageo.2015.02.003.
  • Hardy R. L. (1971). Multiquadric equations of topography and other irregular surfaces. Journal of geophysical research 76:(8) 1905-1915. https://doi.org/10.1029/JB076i008p01905 Hengl T. (2006). Finding the right pixel size. Computers & Geosciences 32:(9) 1283-1298. https://doi.org/10.1016/j.cageo.2005.11.008.
  • Hutchinson M. (1989). A new procedure for gridding elevation and stream line data with automatic removal of spurious pits. Journal of Hydrology 106:(3-4) 211-232. https://doi.org/10.1016/0022-1694(89)90073-5.
  • Hutchinson M. (1996). A locally adaptive approach to the interpolation of digital elevation models. Proceedings, Third International Conference/Workshop on Integrating GIS and Environmental Modeling.
  • Hutchinson M., Bischof R. (1983). A new method for estimating the spatial distribution of mean seasonal and annual rainfall applied to the Hunter Valley, New South Wales. Australian Meteorological Magazine 31:(3) 179-184
  • Hutchinson M. F., Xu T., Stein J. A. (2011). Recent progress in the ANUDEM elevation gridding procedure. Proceedings of the Geomorphometry 19-22
  • Jing C., Shortridge A., Lin S., Wu J. (2014). Comparison and validation of SRTM and ASTER GDEM for a subtropical landscape in Southeastern China. International Journal of Digital Earth 7:(12) 969-992. 10.1080/17538947.2013.807307.
  • Li X., Shen H., Feng R., Li J., Zhang L. (2017). DEM generation from contours and a low-resolution DEM. ISPRS Journal of Photogrammetry and Remote Sensing 134: 135-147. https://doi.org/10.1016/j.isprsjprs.2017.09.014.
  • Li Z., Zhu Q., Gold C. (2005). Digital terrain modeling : principles and methodologyCRC Press New York.
  • Lidberg W., Nilsson M., Ågren A. (2019). Using machine learning to generate high-resolution wet area maps for planning forest management: A study in a boreal forest landscape. Ambio. 10.1007/s13280-019-01196-9.
  • Liu X. (2008). Airborne LiDAR for DEM generation: some critical issues. Progress in Physical Geography 32:(1) 31-49. 10.1177/0309133308089496.
  • Makarovic B. (1977). Regressive rejection—a digital data compression technique. Proc. ASP/ACSM Fall Technical Meeting, Little Rock, USA.
  • Makarovic B. (1984). Structures for geo-information and their application in selective sampling for digital terrain models. ITC journal(4) 285-295Mitas L., Mitasova H. (1999). Spatial interpolation. In P. Longley et al. eds. Geographical information systems: principles, techniques, management and applications. New York, Wiley. 481-492.
  • Mo D., Fuchs H., Fehrmann L., Yang H., Lu Y., Kleinn C. (2015). Local Parameter Estimation of Topographic Normalization for Forest Type Classification. IEEE Geoscience and Remote Sensing Letters 12:(9) 1998-2002. 10.1109/LGRS.2015.2448937.
  • Mukherjee S., Joshi P. K., Mukherjee S., Ghosh A., Garg R. D., Mukhopadhyay A. (2013). Evaluation of vertical accuracy of open source Digital Elevation Model (DEM). International Journal of Applied Earth Observation and Geoinformation 21: 205-217. https://doi.org/10.1016/j.jag.2012.09.004.
  • Nyquist H. (1924). Certain Factors Affecting Telegraph Speed. Transactions of the American Institute of Electrical Engineers XLIII: 412-422. 10.1109/T-AIEE.1924.5060996.
  • Polat N., Uysal M. (2015). Investigating performance of Airborne LiDAR data filtering algorithms for DTM generation. Measurement 63: 61-68. https://doi.org/10.1016/j.measurement.2014.12.017.
  • Rather M. A., Farooq M., Meraj G., Dada M. A., Sheikh B. A., Wani I. A. (2018). Remote Sensing and GIS Based Forest Fire Vulnerability Assessment in Dachigam National Park, North Western Himalaya. Asian Journal of Applied Sciences 11: 98-114
  • Rexer M., Hirt C. (2014). Comparison of free high resolution digital elevation data sets (ASTER GDEM2, SRTM v2.1/v4.1) and validation against accurate heights from the Australian National Gravity Database. Australian Journal of Earth Sciences 61:(2) 213-226. 10.1080/08120099.2014.884983.
  • Robinson A. H., Morrison J. L., Muehrcke P. C., Kimerling A. J., Guptill S. C. (2009). Elements of Cartography. 6th edWiley India Pvt. Limited.
  • Shannon C. E. (1934). Communication in the presence of noise. Proc. Inst. Radio Eng 371.
  • Szabó G., Singh S. K., Szabó S. (2015). Slope angle and aspect as influencing factors on the accuracy of the SRTM and the ASTER GDEM databases. Physics and Chemistry of the Earth, Parts A/B/C 83-84: 137-145. https://doi.org/10.1016/j.pce.2015.06.003.
  • Tachikawa T., Kaku M., Iwasaki A., Gesch D. B., Oimoen M. J., Zhang Z., Danielson J. J., Krieger T., Curtis B., Haase J., Abrams M., Carabajal C. (2011). ASTER Global Digital Elevation Model Version 2 - summary of validation results. 27.
  • Talhofer V., Kovarik V., Rybansky M., Hofmann A., Hubacek M., Hoskova-Mayerova S. (2015). Terrain Analysis for Armed Forces. In J. Brus, A. Vondrakova, V. Vozenilek eds. Modern Trends in Cartography: Selected Papers of CARTOCON 2014. Cham, Springer International Publishing. 519-532.
  • Tarboton D. G. (2003). Terrain analysis using digital elevation models in hydrology. 23rd ESRI international users conference, San Diego, California.
  • Tarolli P., Cavalli M., Masin R. (2019). High-resolution morphologic characterization of conservation agriculture. Catena 172: 846-856. https://doi.org/10.1016/j.catena.2018.08.026.
  • Toutin T. (2002). Impact of terrain slope and aspect on radargrammetric DEM accuracy. ISPRS Journal of Photogrammetry and Remote Sensing 57:(3) 228-240. https://doi.org/10.1016/S0924-2716(02)00123-5.
  • Watson D. F. (1992). Contouring: A Guide to the Analysis and Display of Spatial DataPergamon Press.
  • Wilson J. P., Gallant J. C. (2000). Terrain analysis : principles and applicationsWiley New York.
  • Wong W. V. C., Tsuyuki S., Loki K., Phua M. H. (2014). Accuracy assessment of global topographic data (SRTM & ASTER GDEM) in comparison with lidar for tropical montane forest. Proceedings of the 35th Asian Conference on Remote Sensing, Nay Pyi Taw, Myanmar.
  • Yurtseven H. (2019). Comparison of GNSS-, TLS- and Different Altitude UAV-Generated Datasets on The Basis of Spatial Differences. ISPRS International Journal of Geo-Information 8:(4). 10.3390/ijgi8040175.
  • Yurtseven H., Akgul M., Coban S., Gulci S. (2019). Determination and accuracy analysis of individual tree crown parameters using UAV based imagery and OBIA techniques. Measurement 145: 651-664. https://doi.org/10.1016/j.measurement.2019.05.092.
  • Zhang X., Drake N. A., Wainwright J., Mulligan M. (1999). Comparison of slope estimates from low resolution DEMs: scaling issues and a fractal method for their solution. Earth Surface Processes and Landforms 24:(9) 763-779. 10.1002/(SICI)1096-9837(199908)24:9<763::AID-ESP9>3.0.CO;2-J.
There are 63 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Hüseyin Yurtseven

Publication Date August 20, 2019
Submission Date July 26, 2019
Published in Issue Year 2019 Volume: 7 Issue: 2

Cite

APA Yurtseven, H. (2019). Comparison of ASTER, contour lines and LiDAR based DEMs in terms of topographic differences in forested area. Eurasian Journal of Forest Science, 7(2), 166-186. https://doi.org/10.31195/ejejfs.597460

E-mail: Hbarist@gmail.com 

ISSN: 2147-7493

Eurasian Journal of Forest Science © 2013 is licensed under CC BY 4.0