Next Article in Journal
Evaluating Multispectral Images and Vegetation Indices for Precision Farming Applications from UAV Images
Next Article in Special Issue
Supervised Classification of Benthic Reflectance in Shallow Subtropical Waters Using a Generalized Pixel-Based Classifier across a Time Series
Previous Article in Journal
A Novel Technique for Time-Centric Analysis of Massive Remotely-Sensed Datasets
Previous Article in Special Issue
Operational Actual Wetland Evapotranspiration Estimation for South Florida Using MODIS Imagery
Open AccessArticle

The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands

1
Division of Forestry, Minnesota Department of Natural Resources, 1530 Cleveland Ave. N, St. Paul, MN 55108, USA
2
Department of Forest Resources, University of Minnesota, 1530 Cleveland Ave. N, St. Paul, MN 55108, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Alisa L. Gallant, Deepak Mishra and Prasad S. Thenkabail
Remote Sens. 2015, 7(4), 4002-4025; https://doi.org/10.3390/rs70404002
Received: 1 February 2015 / Revised: 23 March 2015 / Accepted: 27 March 2015 / Published: 2 April 2015
(This article belongs to the Special Issue Towards Remote Long-Term Monitoring of Wetland Landscapes)
Wetlands are dynamic in space and time, providing varying ecosystem services. Field reference data for both training and assessment of wetland inventories in the State of Minnesota are typically collected as GPS points over wide geographical areas and at infrequent intervals. This status-quo makes it difficult to keep updated maps of wetlands with adequate accuracy, efficiency, and consistency to monitor change. Furthermore, point reference data may not be representative of the prevailing land cover type for an area, due to point location or heterogeneity within the ecosystem of interest. In this research, we present techniques for training a land cover classification for two study sites in different ecoregions by implementing the RandomForest classifier in three ways: (1) field and photo interpreted points; (2) fixed window surrounding the points; and (3) image objects that intersect the points. Additional assessments are made to identify the key input variables. We conclude that the image object area training method is the most accurate and the most important variables include: compound topographic index, summer season green and blue bands, and grid statistics from LiDAR point cloud data, especially those that relate to the height of the return. View Full-Text
Keywords: wetlands; optical and infrared sensors; topographic; LiDAR; object based image analysis; segmentation wetlands; optical and infrared sensors; topographic; LiDAR; object based image analysis; segmentation
Show Figures

Figure 1

MDPI and ACS Style

Corcoran, J.; Knight, J.; Pelletier, K.; Rampi, L.; Wang, Y. The Effects of Point or Polygon Based Training Data on RandomForest Classification Accuracy of Wetlands. Remote Sens. 2015, 7, 4002-4025.

Show more citation formats Show less citations formats

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop