TY - GEN

T1 - Quantifying Uncertainty for Estimates Derived from Error Matrices in Land Cover Mapping Applications

T2 - The Case for a Bayesian Approach

AU - Phillipson, Jordan

AU - Blair, Gordon

AU - Henrys, Peter

PY - 2020/2/5

Y1 - 2020/2/5

N2 - The use of land cover mappings built using remotely sensed imagery data has become increasingly popular in recent years. However, these mappings are ultimately only models. Consequently, it is vital for one to be able to assess and verify the quality of a mapping and quantify uncertainty for any estimates that are derived from them in a reliable manner.For this, the use of validation sets and error matrices is a long standard practice in land cover mapping applications. In this paper, we review current state of the art methods for quantifying uncertainty for estimates obtained from error matrices in a land cover mapping context. Specifically, we review methods based on their transparency, generalisability, suitability when stratified sampling and suitability in low count situations. This is done with the use of a third-party case study to act as a motivating and demonstrative example throughout the paper.The main finding of this paper is there is a major issue of transparency for methods that quantify uncertainty in terms of confidence intervals (frequentist methods). This is primarily because of the difficulty of analysing nominal coverages in common situations. Effectively, this leaves one without the necessary tools to know when a frequentist method is reliable in all but a few niche situations. The paper then discusses how a Bayesian approach may be better suited as a default method for uncertainty quantification when judged by our criteria.

AB - The use of land cover mappings built using remotely sensed imagery data has become increasingly popular in recent years. However, these mappings are ultimately only models. Consequently, it is vital for one to be able to assess and verify the quality of a mapping and quantify uncertainty for any estimates that are derived from them in a reliable manner.For this, the use of validation sets and error matrices is a long standard practice in land cover mapping applications. In this paper, we review current state of the art methods for quantifying uncertainty for estimates obtained from error matrices in a land cover mapping context. Specifically, we review methods based on their transparency, generalisability, suitability when stratified sampling and suitability in low count situations. This is done with the use of a third-party case study to act as a motivating and demonstrative example throughout the paper.The main finding of this paper is there is a major issue of transparency for methods that quantify uncertainty in terms of confidence intervals (frequentist methods). This is primarily because of the difficulty of analysing nominal coverages in common situations. Effectively, this leaves one without the necessary tools to know when a frequentist method is reliable in all but a few niche situations. The paper then discusses how a Bayesian approach may be better suited as a default method for uncertainty quantification when judged by our criteria.

KW - Uncertainty quantification

KW - Map assessment

KW - Bayesian

KW - Land cover maps

U2 - 10.1007/978-3-030-39815-6_15

DO - 10.1007/978-3-030-39815-6_15

M3 - Conference contribution/Paper

SN - 9783030398149

T3 - IFIP Advances in Information and Communication Technology

SP - 151

EP - 164

BT - Environmental Software Systems. Data Science in Action

A2 - Athanasiadis, Ioannis N.

A2 - Frysinger, Steven P.

A2 - Schimak, Gerald

A2 - Knibbe, Willem Jan

PB - Springer

CY - Cham

ER -