Abstract
Unmanned aerial systems (UASs) and photogrammetric structure from motion (SFM) algorithms can assist in biomass assessments in tropical countries and can be a useful tool in local greenhouse gas accounting. This study assessed the influence of image resolution, camera type and side overlap on prediction accuracy of biomass models constructed from ground-based data and UAS data in miombo woodlands in Malawi. We compared prediction accuracy of models reflecting two different image resolutions (10 and 15 cm ground sampling distance) and two camera types (NIR and RGB). The effect of two different side overlap levels (70 and 80%) was also assessed using data from the RGB camera. Multiple linear regression models that related the biomass on 37 field plots to several independent 3-dimensional variables derived from five UAS acquisitions were constructed. Prediction accuracy quantified by leave-one-out cross validation increased when using finer image resolution and RGB camera, while coarser resolution and NIR data decreased model prediction accuracy, although no significant differences were observed in absolute prediction error around the mean between models. The results showed that a reduction of side overlap from 80 to 70%, while keeping a fixed forward overlap of 90%, might be an option for reducing flight time and cost of acquisitions. Furthermore, the analysis of terrain slope effect in biomass predictions showed that error increases with steeper slopes, especially on slopes greater than 35%, but the effects were small in magnitude.
Highlights
Tropical forests play a major role in world carbon storage [1], while providing biodiversity and ecological services [2]
We aimed to quantify the effects of: (1) two different RGB and near infrared (NIR) cameras capturing two image resolutions of 10 and 15 cm ground sample distance (GSD), respectively, (2) two levels of side overlap: 70% and 80% using the RGB camera only, (3) transferability of models fitted from one set of acquisition parameters to other acquisitions, which were acquired with different flight characteristics and/or camera types, and (4) terrain slope on biomass predictions
Our results demonstrate that the data generated by Unmanned aerial systems (UASs) with different flight configurations and sensors have the potential to be successfully used in predicting biomass with RMSE% values that ranges from 31.51 to 44.66%
Summary
Tropical forests play a major role in world carbon storage [1], while providing biodiversity and ecological services [2]. Efforts to implement mechanisms that reduce forest deforestation and degradation such as REDD+ could help to stabilize CO2 atmospheric concentrations [1]. The REDD+ mechanism is in the preliminary phases in 33 tropical countries requiring the establishment of administrative structures and the determination of reference levels for carbon stocks [3]. As an example, is targeting 112 small- to medium-sized forest reserves with sizes of up to 2240 ha scattered across the country as potential REDD+ project areas [3]. Carbon stock estimation in these forest reserves requires design and implementation of statistically sound and consistent forest inventories [4]. Field-based forest inventories, which often are associated with large operational and logistical costs, can benefit from remotely sensed information to reduce costs while improving precision of the estimates [5,6,7]
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