Comparison of Office and Field Techniques for Validating Landscape Change Classification in Pacific Northwest National Parks

Catharine Copass,Natalya Antonova,Robert Kennedy

doi:10.3390/rs11010003

Catharine Copass, Natalya Antonova + Show 1 more

Open Access

https://doi.org/10.3390/rs11010003

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Journal: Remote sensing	Publication Date: Dec 20, 2018
Citations: 12	License type: CC BY 4.0

Affiliation: National Park Service, Oregon State University

Abstract

We evaluated the feasibility of using aerial photo-based office methods rather than field-collected data to validate Landsat-based change detection products in national parks in Washington State. Landscape change was performed using LandTrendr algorithm. The resulting change patches were labeled in the office using aerial imagery and a random sample of patches was visited in the field by experienced analysts. Comparison of the two labels and associated confidence shows that the magnitude or severity of the change is a strong indicator of whether field assessment is warranted, and that confusion about patches with lower magnitude changes is not always resolved with a field visit. Our work demonstrates that validation of Landsat-derived landscape change patches can be done using office based tools such as aerial imagery, and that such methods provide an adequate validation for most change types, thus reducing the need for expensive field visits.

Highlights

BackgroundAs computing power has improved and satellite imagery has become more readily available, broad scale, moderate resolution, multi-temporal mapping, classification, and monitoring of landscape change has become increasingly feasible [1,2]
While Tree Toppling had a similar distribution of confidence levels in the office and in the field, size, making methods that are based on aerial photo interpretation conducted in an office setting the reasons for assigning a confidence of two were quite different
A minor Tree Toppling, attractive. This is the case in the large wilderness parks of Washington state, U.S.A., with only a few trees down, was hard to identify on aerial photos, and Progressive Defoliation was where access for field visitation is time consuming and costly, but where inventories of natural often assigned as an alternative label

Summary

Introduction

BackgroundAs computing power has improved and satellite imagery has become more readily available, broad scale, moderate resolution, multi-temporal mapping, classification, and monitoring of landscape change has become increasingly feasible [1,2]. Improved algorithms provide the ability to discern among an ever larger group of change processes, including both anthropogenic and natural events. With this increasing power comes a heightened requirement for validation of landscape change detection products [3]. Field work to collect assessment or validation data is expensive and logistically challenging, for large-area projects that include private land or remote and inaccessible terrain, including mountainous wilderness areas [4]. Strategies to address these challenges vary, including limiting the sample size or making use of existing field validation data collected for different projects and applications. Small sample sizes reduce power, and external data rarely match the temporal and spatial qualities needed to align with the remote sensing products [3]

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