posted on 2021-11-24, 14:04authored byDylan Walshe, Daniel McInerney, João Paulo Pereira, Kenneth A. Byrne
Combining auxiliary variables and field inventory data of forest parameters using the
model-based approach is frequently used to produce synthetic estimates for small areas. These small
areas arise when it may not be financially feasible to take ground measurements or when such areas
are inaccessible. Until recently, these estimates have been calculated without providing a measure of
the variance when aggregating multiple pixel areas. This paper uses a Random Forest algorithm to
produce estimates of quadratic mean diameter at breast height (QMDBH) (cm), basal area (m2 ha−1
),
stem density (n/ha−1
), and volume (m3 ha−1
), and subsequently estimates the variance of multiple
pixel areas using a k-NN technique. The area of interest (AOI) is the state owned commercial forests
in the Slieve Bloom mountains in the Republic of Ireland, where the main species are Sitka spruce
(Picea sitchensis (Bong.) Carr.) and Lodgepole pine (Pinus contorta Dougl.). Field plots were measured
in summer 2018 during which a lidar campaign was flown and Sentinel 2 satellite imagery captured,
both of which were used as auxiliary variables. Root mean squared error (RMSE%) and R2 values
for the modelled estimates of QMDBH, basal area, stem density, and volume were 19% (0.70), 22%
(0.67), 28% (0.62), and 26% (0.77), respectively. An independent dataset of pre-harvest forest stands
was used to validate the modelled estimates. A comparison of measured values versus modelled
estimates was carried out for a range of area sizes with results showing that estimated values in
areas less than 10–15 ha in size exhibit greater uncertainty. However, as the size of the area increased,
the estimated values became increasingly analogous to the measured values for all parameters. The
results of the variance estimation highlighted: (i) a greater value of k was needed for small areas
compared to larger areas in order to obtain a similar relative standard deviation (RSD) and (ii) as
the area increased in size, the RSD decreased, albeit not indefinitely. These results will allow forest
managers to better understand how aspects of this variance estimation technique affect the accuracy
of the uncertainty associated with parameter estimates. Utilising this information can provide forest
managers with inventories of greater accuracy, therefore ensuring a more informed management
decision. These results also add further weight to the applicability of the k-NN variance estimation
technique in a range of forests landscapes.
Funding
Using the Cloud to Streamline the Development of Mobile Phone Apps