Effective identification of terrain positions from gridded DEM data using multimodal classification integration

Terrain positions are widely used to describe the Earth’s topographic features and play an important role in the studies of landform evolution, soil erosion and hydrological modeling. This work develops a new multimodal classification system with enhanced classification performance by integrating different approaches for terrain position identification. The adopted classification approaches include local terrain attribute (LA)-based and regional terrain attribute (RA)-based, rule-based and supervised, and pixel-based and object-oriented methods. Firstly, a double-level definition scheme is presented for terrain positions. Then, utilizing a hierarchical framework, a multimodal approach is developed by integrating different classification techniques. Finally, an assessment method is established to evaluate the new classification system from different aspects. The experimental results, obtained at a Loess Plateau region in northern China on a 5 m digital elevation model (DEM), show reasonably positional relationship, and larger inter-class and smaller intra-class variances. This indicates that identified terrain positions are consistent with the actual topography from both overall and local perspectives, and have relatively good integrity and rationality. This study demonstrates that the current multimodal classification system, developed by taking advantage of various classification methods, can reflect the geographic meanings and topographic features of terrain positions from different levels

DPG particle distribution in etched glass model at different magnifications.

<p>(a)∼(d): 40× magnification; (e)∼(f): 100× magnification.</p

The profile improvement capacity of DPG particles.

<p>The profile improvement capacity of DPG particles.</p

Visual simulation results in a flat panel sand model.

<p>(a) model; (b) saturating water; (c) saturating oil; (d) water flooding until water cut is up to 98%; (e) injection of DPG particles for 6 min; (f) injection of DPG particles for 9 min; (g) injection of DPG particles for 15 min; (h) injection of DPG particles for 20 min; (o) water flooding until water cut is up to 98% again.</p

Schematic illustration for the enhanced oil recovery mechanism.

<p>(a) oil distribution in the initial stage of reservoir development; (b) a high permeability zone formed after long-term water flooding; (c) injection of DPG particles for profile control; (d) DPG particles deform and pass through pores; (c) water flooding after the treatment.</p

Visual simulation results in etched glass model.

<p>(a) model; (b) saturating water; (c: saturating oil; (d) water flooding until water cut is up to 98%; (e) retention in larger pore space; (f) directly plugging the small pore throat; (g) segregated flow of oil and water pathway; (h) adsorption on the surface; (o) water flooding until water cut is up to 98% again.</p

The multi-point pressure physical model.

<p>The multi-point pressure physical model.</p

Disproportionate permeability reduction of DPG particles.

<p>Disproportionate permeability reduction of DPG particles.</p

The pressure changes at different stages.

<p>The pressure changes at different stages.</p

The morphology and size distribution of typical DPG particles.

<p>(A) The TEM morphology of nano-size DPG particles; (a) Nano-size DPG particles with an average size of approximately 108 nm; (B) The TEM morphology of micron-size DPG particles; (b) Nano-size DPG particles with an average size of approximately 5.6 µm; (C) mm-Size DPG particles with an average size of approximately 3.2 mm.</p