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Assessing potential of UAV multispectral imagery for estimation of AGB and carbon stock in conifer forest over UAV RGB imagery

Gaden, Kezang (2020) Assessing potential of UAV multispectral imagery for estimation of AGB and carbon stock in conifer forest over UAV RGB imagery.

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Abstract:The information on forest biomass and carbon stock is essential to monitor and report national greenhouse gas (GHG) inventories to UNFCCC. Forestry is one of the crucial sectors in a national GHG inventory as deforestation and forest degradation is the second critical drivers of climate change. Conifer forest plays a vital role in the global carbon cycle by sequestering carbon dioxide from the atmosphere due to its fast growth. Field-based inventory and remote sensing (RS) are both recommended by UNFCCC to assess forest biomass and carbon stock for REDD+. RS method is considered to be more efficient over the costly traditional forest inventory for large scale assessments. Among widely available remote sensing data, UAV images allow retrieving individual tree parameters owing to its high image resolution. Studies have found UAV RGB imagery suitable for estimating aboveground biomass or carbon (AGB/AGC) required for reporting emissions related to changes in forest biomass. However, there is hardly any study on estimation of AGB/AGC using UAV multispectral (MS) imagery with structure from motion (SfM) technique. UAV MS imagery with the high spectral resolution is expected to model DBH and estimate AGB/AGC better than UAV RGB imagery. Therefore, this study aims to evaluate the potential of UAV MS imagery to estimate AGB/AGC over the UAV RGB imagery in a part of temperate conifer forest. The study was conducted in Snippert forest of west Lonneker, The Netherlands. Diameter at breast height (DBH) and tree height of 650 trees were measured in 35 plots selected based on simple random sampling method. UAV MS images were obtained from Parrot Sequoia MS sensor, while UAV RGB images were obtained from Phantom 4 RGB camera and processed using SfM technique in Pix4Dmapper. MS and RGB-based crown diameter were derived from canopy projection area to model DBH, and their relationship was assessed. UAV MS and RGB tree height were derived from the respective canopy height model, and their accuracies were assessed using LiDAR tree height obtained from Actueel Hoogtebestand Nederland (AHN). Regression models were compared to determine how accurately the DBH can be estimated using UAV-derived parameters. For regression models, field-measured DBH was used as a dependent variable and UAV-derived parameters such as tree height, canopy projection area, crown diameter and the combination of tree height and crown diameter as independent variables. The accuracy of the estimated DBH was evaluated using validation dataset from field-measured DBH. A species-specific allometric equation was used to estimate UAV-based AGB/AGC and compared with field LiDAR-based AGB/AGC. A set of orthomosaic, DSM and DTM were generated from respective UAV MS and RGB images. The study found a strong positive correlation (r = 0.98) between UAV MS and RGB-derived crown diameter, indicating the suitability of retrieving crown diameter from UAV MS imagery to estimate DBH. UAV MS-derived tree height (R2 = 0.79) was slightly less accurate than UAV RGB-derived tree height (R2 = 0.83). However, a higher deviation was observed in RGB-derived tree height (RMSE = 2.95 m) compared to MS-derived tree height (RMSE = 1.94 m) which is attributed to a high spatial resolution of UAV RGB images. Quadratic model of both MS and RGB showed the higher model performance to predict DBH. Using validation dataset, MS model (R2 = 0.82; RMSE = 4.36 cm) estimated DBH more accurate than RGB model (R2 = 0.80; RMSE = 4.53 cm). Mean AGB assessed from the field with LiDAR-measured parameter was 8.49 Mg plot-1 (i.e. 169.83 Mg ha-1). In contrast, the mean AGB estimated from UAV MS and RGB imagery was 8.68 and 9.06 Mg plot-1 (i.e. 173.52 and 181.24 Mg ha-1), respectively. As expected, the accuracy of AGB estimated from MS-derived parameters (R2 = 0.91; RMSE = 149.71 kg) was higher than RGB-derived parameters (R2= 0.89; RMSE = 166.85 kg), which is explained by higher accuracy of DBH modelled from MS-derived parameters. Therefore, this study concludes that UAV MS imagery is suitable to estimate AGB/AGC, and performs better than UAV RGB imagery suggesting a promising application for REDD+ monitoring and forest management practices in a managed coniferous forest at a local scale.
Item Type:Essay (Master)
Faculty:ITC: Faculty of Geo-information Science and Earth Observation
Programme:Geoinformation Science and Earth Observation MSc (75014)
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