Next Article in Journal
Automatic Detection of Potential Dam Locations in Digital Terrain Models
Previous Article in Journal
Application of Hierarchical Spatial Autoregressive Models to Develop Land Value Maps in Urbanized Areas
Open AccessArticle

Quantifying Efficiency of Sliding-Window Based Aggregation Technique by Using Predictive Modeling on Landform Attributes Derived from DEM and NDVI

by Rahul Gomes 1,*,†,‡, Anne Denton 1,‡ and David Franzen 2,‡
1
Department of Computer Science, North Dakota State University, Fargo, ND 58102, USA
2
Department of Soil Science, North Dakota State University, Fargo, ND 58102, USA
*
Author to whom correspondence should be addressed.
Current address: 315 Model Hall, 500 University Ave. W. Minot, ND 58707, USA.
These authors contributed equally to this work.
ISPRS Int. J. Geo-Inf. 2019, 8(4), 196; https://doi.org/10.3390/ijgi8040196
Received: 10 March 2019 / Revised: 21 April 2019 / Accepted: 22 April 2019 / Published: 24 April 2019
Topographic features impact biomass and other agriculturally relevant observables. However, conventional tools for processing digital elevation model (DEM) data in geographic information systems have severe limitations. Typically, 3-by-3 window sizes are used for evaluating the slope, aspect and curvature. As a consequence, high resolution DEMs have to be resampled to match the size of typical topographic features, resulting in low accuracy and limiting the predictive ability of any model using such features. In this paper, we examined the usefulness of DEM-derived topographic features within Random Forest models that predict biomass. Our model utilized the derived topographic features and achieved 95.31% accuracy in predicting Normalized Difference Vegetation Index (NDVI) compared to a 51.89% accuracy obtained for window size 3-by-3 in the traditional resampling model. The efficacy of partial dependency plots (PDP) in terms of interpretability was also assessed. View Full-Text
Keywords: sliding window; Random Forest; DEM; NDVI; curvature; slope; aspect; partial dependence plots sliding window; Random Forest; DEM; NDVI; curvature; slope; aspect; partial dependence plots
Show Figures

Figure 1

MDPI and ACS Style

Gomes, R.; Denton, A.; Franzen, D. Quantifying Efficiency of Sliding-Window Based Aggregation Technique by Using Predictive Modeling on Landform Attributes Derived from DEM and NDVI. ISPRS Int. J. Geo-Inf. 2019, 8, 196.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop