How to calibrate historical aerial photographs : a change analysis of naturally dynamic boreal forest landscapes
|Author:||Kulha, Niko1; Pasanen, Leena2; Aakala, Tuomas1|
1Department of Forest Sciences, University of Helsinki
2Research Unit of Mathematical Sciences, University of Oulu
|Online Access:||PDF Full Text (PDF, 3.3 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2018111448149
Multidisciplinary Digital Publishing Institute,
|Publish Date:|| 2018-11-14
Time series of repeat aerial photographs currently span decades in many regions. However, the lack of calibration data limits their use in forest change analysis. We propose an approach where we combine repeat aerial photography, tree-ring reconstructions, and Bayesian inference to study changes in forests. Using stereopairs of aerial photographs from five boreal forest landscapes, we visually interpreted canopy cover in contiguous 0.1-ha cells at three time points during 1959–2011. We used tree-ring measurements to produce calibration data for the interpretation, and to quantify the bias and error associated with the interpretation. Then, we discerned credible canopy cover changes from the interpretation error noise using Bayesian inference. We underestimated canopy cover using the historical low-quality photographs, and overestimated it using the recent high-quality photographs. Further, due to differences in tree species composition and canopy cover in the cells, the interpretation bias varied between the landscapes. In addition, the random interpretation error varied between and within the landscapes. Due to the varying bias and error, the magnitude of credibly detectable canopy cover change in the 0.1-ha cells depended on the studied time interval and landscape, ranging from −10 to −18 percentage points (decrease), and from +10 to +19 percentage points (increase). Hence, changes occurring at stand scales were detectable, but smaller scale changes could not be separated from the error noise. Besides the abrupt changes, also slow continuous canopy cover changes could be detected with the proposed approach. Given the wide availability of historical aerial photographs, the proposed approach can be applied for forest change analysis in biomes where tree-rings form, while accounting for the bias and error in aerial photo interpretation.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
112 Statistics and probability
The project was funded by the Academy of Finland (Proj. No. 252629, 276022), Emil Aaltonen Foundation, and the University of Helsinki Funds.
|Academy of Finland Grant Number:||
252629 (Academy of Finland Funding decision)
276022 (Academy of Finland Funding decision)
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).