Next Article in Journal
Downscaling of ASTER Thermal Images Based on Geographically Weighted Regression Kriging
Previous Article in Journal
Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches
Open AccessArticle

Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach

1
School of Natural Resources and the Environment, University of Arizona, Tucson, AZ 85721, USA
2
School of Geography and Development, University of Arizona, Tucson, AZ 85721, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(4), 632; https://doi.org/10.3390/rs10040632
Received: 21 February 2018 / Revised: 13 April 2018 / Accepted: 17 April 2018 / Published: 19 April 2018
(This article belongs to the Section Forest Remote Sensing)
Woody cover encroachment/expansion/conversion is a complex phenomenon that has environmental and economic impacts around the world. This research demonstrates the development of highly accurate models for estimating percent woody cover using high spatial resolution image data in combination with multi-seasonal Landsat reflectance products. We use a classification and regression tree (CART) approach to classify woody cover using fine resolution multispectral National Agricultural Imaging Program (NAIP) data. A continuous classification and regression tree (Cubist) ingests the aggregated woody cover classification along with the seasonal Landsat data to create a continuous woody cover model. We applied the models, derived by Cubist, across several Landsat scenes to estimate the percentage of woody plant cover, within each Landsat pixel, over a larger regional extent. We measured an average absolute error of 12.1 percent and a correlation coefficient of 0.78 for the models performed. The method of modelling percent woody cover established in this manuscript outperforms currently available woody cover estimates including Landsat Vegetation Continuous Fields (VCF), on average by 26 percent, and Web-Enabled Landsat Data (WELD) products, on average by 16 percent, for the region of interest. Current woody cover products are also limited to certain years and not available pre-2000. This manuscript describes a novel Cubist-based technique to model woody cover for any area of the world, as long as fine (~1–2 m) spatial resolution and Landsat data are available. View Full-Text
Keywords: woody cover; Cubist; modelling; Landsat; NAIP; CART; Texas; Oklahoma woody cover; Cubist; modelling; Landsat; NAIP; CART; Texas; Oklahoma
Show Figures

Graphical abstract

MDPI and ACS Style

Hartfield, K.A.; Van Leeuwen, W.J.D. Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach. Remote Sens. 2018, 10, 632.

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