Improved k-NN Mapping of Forest Attributes in Northern Canada Using Spaceborne L-Band SAR, Multispectral and LiDAR Data

André Beaudoin,Philippe Villemaire,Michelle Filiatrault,Luc Guindon,Ronald J Hall,Guillermo Castilla,Rob Skakun

doi:10.3390/rs14051181

Abstract

Satellite forest inventories are the only feasible way to map Canada’s vast, remote forest regions, such as those in the Northwest Territories (NWT). A method used to create such inventories is the k-nearest neighbour (k-NN) algorithm, which spatially extends information from forest inventory (FI) plots to the entire forest land base using wall-to-wall features typically derived from Landsat data. However, the benefits of integrating L-band synthetic aperture radar (SAR) data, strongly correlated to forest biomass, have not been assessed for Canadian northern boreal forests. Here we describe an optimized multivariate k-NN implementation of a 151,700 km2 area in southern NWT that included ca. 2007 Landsat and dual-polarized Phased Array type L-band SAR (PALSAR) data on board the Advanced Land Observing Satellite (ALOS). Five forest attributes were mapped at 30 m cells: stand height, crown closure, stand/total volume and aboveground biomass (AGB). We assessed accuracy gains compared to Landsat-based maps. To circumvent the scarcity of FI plots, we used 3600 footprints from the Geoscience Laser Altimeter System (GLAS) as surrogate FI plots, where forest attributes were estimated using Light Detection and Ranging (LiDAR) metrics as predictors. After optimization, k-NN predicted forest attribute values for each pixel as the average of the 4 nearest (k = 4) surrogate FI plots within the Euclidian space of 9 best features (selected among 6 PALSAR, 10 Landsat, and 6 environmental features). Accuracy comparisons were based on 31 National Forest Inventory ground plots and over 1 million airborne LiDAR plots. Maps that included PALSAR HV backscatter resulted in forest attribute predictions with higher goodness of fit (adj. R2), lower percent mean error (ME%), and percent root mean square error (RMSE%), and lower underestimation for larger attribute values. Predictions were most accurate for conifer stand height (RMSE% = 32.1%, adj. R2 = 0.58) and AGB (RMSE% = 47.8%, adj. R2 = 0.74), which is much more abundant in the area than mixedwood or broadleaf. Our study demonstrates that optimizing k-NN parameters and feature space, including PALSAR, Landsat, and environmental variables, is a viable approach for inventory mapping of the northern boreal forest regions of Canada.

Highlights

Information on forest composition and structure is necessary to support forest ecosystem management [1,2]
Other factors could have contributed to the differences we found in our comparative accuracy assessment of the Satellite Vegetation Inventory (SVI) maps and the two previously published maps, namely, differences in training set, Landsat and environmental feature variables, and choice of machine learning algorithm
This paper describes the multivariate k-nearest neighbour (k-NN) implementation approach followed in the MVI project [5] to map forest attributes in the northern boreal forests of Northwest Territories (NWT), Canada, and assesses improvements in predictive performance achieved through the addition of

Summary

Introduction

Information on forest composition and structure is necessary to support forest ecosystem management [1,2]. In Canada, this information is typically captured in the form of forest inventory maps derived from the interpretation of aerial photographs [3,4]. There has been considerable interest in exploring the use of remote sensing at various spatial scales in boreal forests of Canada, including the NWT [6–8]. The application of remote sensing for estimating and mapping forest inventory attributes such as stand height, crown closure, timber volume, and aboveground biomass (AGB) has evolved considerably. This is in part due to the rapid development of airborne and satellite sensors operating at different spatial and temporal scales and their methods of analysis, which can supplement or enhance forest inventory programs [2,9,10]. The vintage of ALS data is a consideration, as the timing of ALS aerial survey and in-situ field measurements should be similar to ensure the data are suitable for calibration and are representative of the forest landscape to be mapped [14]

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