A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal–Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning

• Published in: Remote sensing (Basel, Switzerland) Vol. 11; no. 15; p. 1804
• Main Authors: Næsset, Erik, Gobakken, Terje, McRoberts, Ronald E.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2019
• Subjects: Accuracy; Airborne lasers; Biomass; Climate change; Covariance; Data acquisition; Domains; ecotones; Estimates; Estimators; Forest monitoring; Forests; Global change; Identification; landscapes; Laser scanning; Lasers; lidar; Mathematical models; Model-dependent inference; Monitoring; Monitoring methods; Mountains; Norway; Parameter uncertainty; prediction; Regression analysis; Regression models; Remote sensing; scanners; Sensors; Small trees; Statistical analysis; Statistical inference; Taiga & tundra; Tree growth; tree height; Tree migration; treeline; Trees; Uncertainty analysis; variance; Vegetation; United States > US; Norway
• DOI: 10.3390/rs11151804
• ISSN: 2072-4292

The boreal tree line is in many places expected to advance upwards into the mountains due to climate change. This study aimed to develop a general method for estimation of vegetation height change in general, and change in tree height more specifically, for small geographical domains utilizing bi-temporal airborne laser scanner (ALS) data. The domains subject to estimation may subsequently be used to monitor vegetation and tree height change with detailed temporal and geographical resolutions. A method was developed with particular focus on statistically rigorous estimators of uncertainty for change estimates. The method employed model-dependent statistical inference. The method was demonstrated in a 12 ha study site in a boreal–alpine tree line in southeastern Norway, in which 316 trees were measured on the ground in 2006 and 2012 and ALS data were acquired in two temporally coincident campaigns. The trees ranged from 0.11 m to 5.20 m in height. Average growth in height was 0.19 m. Regression models were used to predict and estimate change. By following the area-based approach, predictions were produced for every individual 2 m2 population element that tessellated the study area. Two demonstrations of the method are provided in which separate height change estimates were calculated for domains of size 1.5 ha or greater. Differences in height change estimates among such small domains illustrate how change patterns may vary over the landscape. Model-dependent mean square error estimates for the height change estimators that accounted for (1) model parameter uncertainty, (2) residual variance, and (3) residual covariance are provided. Findings suggested that the two latter sources of uncertainty could be ignored in the uncertainty analysis. The proposed estimators are likely to work well for estimation of differences in height change along a gradient of small monitoring units, like the 1.5 ha cells used for demonstration purposes, and thus may potentially be used to monitor tree line migration over time.