Date of Award
2019
Publisher
North Dakota State University
Document Type
Dissertation
Degree Awarded
Doctor of Philosophy (PhD)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Denton, Anne M.
Description
The resolution of spatial data has increased over the past decade making them more accurate in depicting landform features. From using a 60m resolution Landsat imagery to resolution close to a meter provided by data from Unmanned Aerial Systems, the number of pixels per area has increased drastically. Topographic features derived from high resolution remote sensing is relevant to measuring agricultural yield. However, conventional algorithms in Geographic Information Systems (GIS) used for processing digital elevation models (DEM) have severe limitations. Typically, 3-by-3 window sizes are used for evaluating the slope, aspect and curvature. Since this window size is very small compared to the resolution of the DEM, they are mostly resampled to a lower resolution to match the size of typical topographic features and decrease processing overheads. This results in low accuracy and limits the predictive ability of any model using such DEM data. In this dissertation, the landform attributes were derived over multiple scales using the concept of sliding window-based aggregation. Using aggregates from previous iteration increases the efficiency from linear to logarithmic thereby addressing scalability issues. The usefulness of DEM-derived topographic features within Random Forest models that predict agricultural yield was examined. The model utilized these derived topographic features and achieved the highest accuracy of 95.31% in predicting Normalized Difference Vegetation Index (NDVI) compared to a 51.89% for window size 3-by-3 in the conventional method. The efficacy of partial dependence plots (PDP) in terms of interpretability was also assessed. This aggregation methodology could serve as a suitable replacement for conventional landform evaluation techniques which mostly rely on reducing the DEM data to a lower resolution prior to data processing.
Recommended Citation
Gomes, Rahul, "Incorporating Sliding Window-Based Aggregation for Evaluating Topographic Variables in Geographic Information Systems" (2019). Computer Science. 31.
https://digitalcommons.ndsu.edu/computer-sci/31
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Author ORCID Identifier
0000-0002-5377-8196
Handle Identifier
https://hdl.handle.net/10365/29913