22.5.6.2 Landform Analysis, Landform Description

Chapter Contents (Back)
Landform.

Gökgöz, T.[Türkay], Baker, M.K.M.[Moustafa Khalil M.],
Large Scale Landform Mapping Using Lidar DEM,
IJGI(4), No. 3, 2015, pp. 1336.
DOI Link 1508
BibRef

Guilbert, E.[Eric], Moulin, B.[Bernard],
Towards a Common Framework for the Identification of Landforms on Terrain Models,
IJGI(6), No. 1, 2017, pp. xx-yy.
DOI Link 1702
BibRef

Kramm, T.[Tanja], Hoffmeister, D.[Dirk], Curdt, C.[Constanze], Maleki, S.[Sedigheh], Khormali, F.[Farhad], Kehl, M.[Martin],
Accuracy Assessment of Landform Classification Approaches on Different Spatial Scales for the Iranian Loess Plateau,
IJGI(6), No. 11, 2017, pp. xx-yy.
DOI Link 1712
BibRef

Mayoral, A.[Alfredo], Toumazet, J.P.[Jean-Pierre], Simon, F.X.[François-Xavier], Vautier, F.[Franck], Peiry, J.L.[Jean-Luc],
The Highest Gradient Model: A New Method for Analytical Assessment of the Efficiency of LiDAR-Derived Visualization Techniques for Landform Detection and Mapping,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703
BibRef

Carbonell-Carrera, C.[Carlos], Hess-Medler, S.[Stephany],
3D Landform Modeling to Enhance Geospatial Thinking,
IJGI(8), No. 2, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Castillejo-González, I.L.[Isabel Luisa], Angueira, C.[Cristina], García-Ferrer, A.[Alfonso], Sánchez de la Orden, M.[Manuel],
Combining Object-Based Image Analysis with Topographic Data for Landform Mapping: A Case Study in the Semi-Arid Chaco Ecosystem, Argentina,
IJGI(8), No. 3, 2019, pp. xx-yy.
DOI Link 1903
BibRef

Gomes, R.[Rahul], Denton, A.[Anne], Franzen, D.[David],
Quantifying Efficiency of Sliding-Window Based Aggregation Technique by Using Predictive Modeling on Landform Attributes Derived from DEM and NDVI,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Du, L.[Lin], You, X.[Xiong], Li, K.[Ke], Meng, L.Q.[Li-Qiu], Cheng, G.[Gong], Xiong, L.Y.[Li-Yang], Wang, G.X.[Guang-Xia],
Multi-modal deep learning for landform recognition,
PandRS(158), 2019, pp. 63-75.
Elsevier DOI 1912
Landform recognition, Multi-modal geomorphological data fusion, Deep learning, Convolutional neural networks (CNN) BibRef

Yang, X.W.[Xian-Wu], Tang, G.[Guoan], Meng, X.[Xin], Xiong, L.Y.[Li-Yang],
Classification of Karst Fenglin and Fengcong Landform Units Based on Spatial Relations of Terrain Feature Points from DEMs,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
BibRef

Mihu-Pintilie, A.[Alin], Nicu, I.C.[Ionut Cristi],
GIS-based Landform Classification of Eneolithic Archaeological Sites in the Plateau-plain Transition Zone (NE Romania): Habitation Practices vs. Flood Hazard Perception,
RS(11), No. 8, 2019, pp. xx-yy.
DOI Link 1905
BibRef

Li, L.L.[Lei-Lei], Yang, J.T.[Jin-Tao], Wu, J.[Jin],
A Method of Watershed Delineation for Flat Terrain Using Sentinel-2A Imagery and DEM: A Case Study of the Taihu Basin,
IJGI(8), No. 12, 2019, pp. xx-yy.
DOI Link 1912
BibRef


Eisank, C., Dragut, L.D., Götz, J., Blaschke, T.,
Developing a Semantic Model of Glacial Landforms for Objectbased Terrain Classification: The Example of Glacial Cirques,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Camargo, F.F., Almeida, C.M., Costa, G.A.O.P., Feitosa, R.Q., Oliveira, D.A.B., Ferreira, R.S., Heipke, C.,
Cognitive Approaches and Optical Multispectral Data for Semiautomated Classification of Landforms in a Rugged Mountainous Area,
GEOBIA10(xx-yy).
PDF File. 1007
BibRef

Chapter on Remote Sensing, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Bathymetric Mapping, Analysis, Underwater Terrain .


Last update:Feb 20, 2020 at 21:34:09