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

1,*,†,‡, 1,‡ and 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
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Abstract

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
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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.

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