Abstract
Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km 2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient ( r ) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.
Highlights
Canopy cover is defined as the fraction of ground covered by the vertically projected tree crowns, which plays an important role in affecting forest succession, evaluating carbon fluxes, and managing wildlife habitats [1,2,3,4,5]
Several global maps have been produced based on satellite images with low or moderate spatial resolutions, such as those acquired by Advanced Very High Resolution Radiometer (AVHRR) from the NOAA, Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites and Landsat
The other was distributed by the Global Land Analysis and Discovery laboratory, which has two maps centered on years 2000 and 2010, respectively [7]
Summary
Canopy cover is defined as the fraction of ground covered by the vertically projected tree crowns, which plays an important role in affecting forest succession, evaluating carbon fluxes, and managing wildlife habitats [1,2,3,4,5]. One was developed by the Global Land Cover Facility, which was composed of four maps centered on years 2000, 2005, 2010, and 2015, respectively (hereafter referred to as the GLCF canopy cover) [4]. The other was distributed by the Global Land Analysis and Discovery laboratory, which has two maps centered on years 2000 and 2010, respectively (hereafter referred to as the GLAD canopy cover) [7]. UAVbased canopy covers are further used as references to evaluate the aforementioned satellite-based maps in Daxing’anling forested area.
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