University of Twente Student Theses


A comparison between UAV-RGB and ALOS-2 PALSAR-2 images for the assessment of aboveground biomass in a temperate forest

Ahmed, Hasan (2021) A comparison between UAV-RGB and ALOS-2 PALSAR-2 images for the assessment of aboveground biomass in a temperate forest.

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Abstract:Forests play a significant role in global warming mitigation strategies. The Netherlands and other nations committed to reducing global warming must assess and monitor forest biomass/carbon. National forest carbon inventories are mostly based on the estimation of the aboveground biomass (AGB). Remote sensing methods, in addition to field-based approaches, are applied to assess forest AGB. UAV RGB Orthomosaic and ALOS-2 PALSAR-2 images are two of many remote sensing data to estimate forest AGB. UAV RGB images provide very-high-resolution images that are used to identify tree crowns. Related parameters such as DBH are modeled from those tree crowns, and finally, the AGB of the tree is estimated. However, the UAV RGB sensor is a passive sensor that cannot penetrate the surface of the canopy and does not include trees suppressed by taller trees. Conversely, ALOS-2 PALSAR-2 is an active remote sensing sensor (L-band SAR) that can penetrate the forest's canopy and sometimes reach the top of the soil layer. Therefore, PALSAR-2 backscatter contains information from the forest canopy, trunks and soil. The method to estimate AGB from PALSAR-2 backscatter is straightforward by developing a regression model between the AGB and backscatter coefficients. However, PALSAR-2 provides AGB information in low resolution, and the backscatter saturates with increasing AGB value. Both of the sensors have limitations in assessing area-based AGB of the forest; UAV does not include suppressed trees, and PALSAR-2 gives biomass information at low resolution and is limited by backscatter saturation. In this regard, this study aimed to compare the plot-based forest biomass estimated from UAV and ALOS-2 PALSAR-2 in a temperate forest and assess their accuracy. Forest parameters such as DBH and the height of 1584 trees have been collected from 94 sample plots. AGB of each individual tree was calculated from the parameters collected parameters by using species-specific allometric equations. Plot AGB was derived from the individual tree AGBs. This study used two standard methods of AGB estimation from UAV RGB and ALOS-2 PALSAR-2 images. In the case of UAV RGB images, we delineated the CPA of trees manually and then used the CPA-DBH relationship grouped into conifers and broadleaves to model DBH. Modeled DBH was used in species-specific allometric equations to obtain UAV estimated individual tree AGB. Then the individual tree AGB modeled from UAV RGB images was transformed into plot AGB. On the other hand, HH and HV polarization backscatter coefficients of the PALSAR-2 image were extracted for each plot by setting a 9-pixels (3x3) window and taking the average of the coefficients. Then a regression between field-measured AGB and backscatter coefficients was established to model AGB from the backscatter coefficients. The study found a positive correlation between CPA delineated from UAV RGB and DBH at a coefficient of determination of 0.89 for broadleaves and 0.92 for conifers with RMSE of 4.28 cm and 2.44 cm accordingly. Individual tree AGB estimated from UAV RGB images depicted a strong correlation with biometric AGB (R2 = 0.81). However, the plot-based AGB estimation resulted in a high amount of underestimation and overestimation in several plots. UAV RGB images modeled plot AGB had a poor correlation with biometric AGB (R2 = 0.35, RMSE= 57.18 tons/ha). In the case of PALSAR-2, HV backscatter had a better relationship with AGB. The logarithmic relationship between AGB and HV backscatter represented a high correlation at R2 = 0.85 with RMSE = 40.9 tons/ha. Moreover, this study also found that plot AGB is better estimated from both UAV RGB and ALOS-2 PALSAR-2 images in coniferous forest stand compared to broadleaves and mixed forest stands. Based on our analysis, we concluded that ALOS-2 PALSAR-2 is a better choice over UAV RGB to estimate the area-based AGB of a temperate forest with intermingling crowns and dense canopy. However, we also remarked that UAV RGB could be better in individual tree-based assessment in a non-intermingling crown forest stands and assessing how PALSAR-2 backscatter estimates AGB of an open forest with non-intermingling crowns could lead to a comprehensive conclusion.
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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