University of Twente Student Theses


Calibrating a VHR sensor based aboveground biomass model with UAV footprints in a Dutch temperate forest

Figueroa Sanchez, Luis Alonso (2021) Calibrating a VHR sensor based aboveground biomass model with UAV footprints in a Dutch temperate forest.

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Abstract:Forests play a vital role in the sequestration of carbon dioxide from the atmosphere, this in turn mitigates climate change. The carbon stored in forests can be found in different pools. Aboveground biomass (AGB) is one of the main pools that is most commonly monitored. As anthropogenic pressure on these ecosystems increases in the form of deforestation and forest degradation, reliable methods for the quantification of AGB over extensive areas have to be developed. Allometric equations can be used to estimate AGB by using biometric tree data. In large areas, this is time consuming and non-practical. Therefore, the UNFCCC has promoted the use of remote sensing technology to achieve this task. Unmanned Aerial Vehicles (UAVs) and satellite constellations are earth observation technologies that have been used extensively in forestry applications. UAVs are known to be highly customizable and easily operatable whilst providing very high spatial resolution data over small areas. Satellite constellations are exploring the boundaries of big geodata by providing high spatial resolution data in shorter revisit times, but have the disadvantage of providing small spectral resolutions. Previous research has used these remote sensing technologies in combination to map AGB. Linear regressions have been widely used to relate AGB and an explanatory feature derived from the sensor in order to map AGB. But linear regressions have been established to relate both sensors resulting in high errors at very high spatial resolutions. The addition of UAV data and machine learning algorithms may solve previous shortcomings. This study aims at estimating AGB through the use of a combination of UAV data, high spatial resolution satellite imagery and machine learning algorithms in a mixed temperate forest, Haagse Bos, Netherlands. A model calibration approach is proposed for this study in which the satellite AGB model is based on the output of a UAV AGB model. To achieve this, an object-based image analysis was implemented to segment coniferous and broadleaf tree species to obtain explanatory features from UAV data. The accuracy of the watershed segmentation was evaluated by using three performance metrics: over segmentation, under segmentation and total segmentation error. A total of 42 explanatory features were obtained based on multispectral layers, vegetation indices, canopy height model and gray-level co-occurrence matrices. Random Forest (RF) and Support Vector Machine (SVM) regression algorithms were used to predict AGB based on the explanatory features. Based on the UAV AGB estimations, explanatory features were extracted from the satellite image at a pixel level. The RF and SVM algorithms were again assessed by the performance metrics calculated from a 10-fold cross validation and a test set. The study’s analysis showed that the estimations of AGB performed better when generating two separate models for coniferous and broadleaf tree species in both the UAV and satellite stage. For the estimation of AGB with the UAV data, the information provided by the canopy height model gave the most predictive power to both models. Following this explanatory feature, the coniferous regression model preferred the texture layers while the broadleaf model gained more information with the red band layer and the crown projected area of each canopy. Both tree types recorded their best performance in the SVM regression algorithm. With only the 15 most important explanatory features, the coniferous model obtained the highest R2 of 73.7%. The broadleaf model obtained its highest R2 of 62.6% with the tops nine features. In the satellite data, the inclusion of elevation data was necessary to improve the results of the regression models. The canopy height model was the most important feature for both predictive models. In both cases, the Random Forest algorithm outperformed the performance metrics of the SVM algorithm. The highest R2 recorded for the coniferous tree species was of 54.0% by using the top 13 explanatory features. The broadleaf model recorded a lower performance in comparison. Using the 20 most important features, an R2 of 43.6% was obtained. The moderate performances of the VHR model can be attributed to the error propagation provided by the location of the measured trees, individual tree segmentation, and overestimation and underestimation of the UAV regression models.
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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