Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland

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.

A Deep Learning Semantic Segmentation-Based Approach for Field-Level Sorghum Panicle Counting

“…Also, this study did not address the impact of changes in the image spatial resolution on the panicle detection. However, previous studies on UAS imaging performance have shown that lower resolution data tend to reduce the accuracy of derived metrics e.g., plant height and biomass [69][70][71]. Other studies have also shown that lower resolution images tend to lower the performance of deep learning models [72,73].…”

A Systematic Review of the Factors Influencing the Estimation of Vegetation Aboveground Biomass Using Unmanned Aerial Systems

“…Some studies used spectral information [2,11,18,24,26,43,58,[60][61][62] and some structural information [1,8,22,23,28,34,48,50,55,[63][64][65]. Others used both [3][4][5][6]9,12,16,20,21,25,30,33,49,57,[66][67][68], while a few studies used spectral and structural metrics plus another data type [13,27,69] (Table A1). Within these categories, a wide range of species, study areas and methods are examined, demonstrating the applicability of UAS data to AGB estimation in agricultural and non-agricultural environments.…”

“…We found 15 research papers [1,8,19,22,23,28,34,48,50,55,[62][63][64]74,75] that used structural measurements alone and 12 papers [4,5,9,12,15,16,20,21,25,30,49,57] that used structural metrics along with spectral data to estimate biomass of vegetation (Table A1). All structural variables used by studies in this review are listed in Table 1.…”

“…Mean height [3,9,12,13,15,16,[19][20][21]23,25,28,30,34,[48][49][50]57,58,63,65,[67][68][69][74][75][76] Maximum height [1,3,4,13,28,30,34,48,57,63,65,69] Minimum height [3,28,34,48,57,63,65,69] Median height [12,21,27,48,63,65,…”

“…There is no standardized methodology for planning, collecting and analyzing these data to derive AGB information. Numerous factors related to data collection and analysis methods and the study species and area of interest have the potential to affect the accuracy and predictive capabilities of derived models [2,8,57,58]. Without careful consideration of these factors, AGB estimation may be biased or imprecise, resulting in decreased accuracy of AGB models with potentially negative consequences for inferences and management decisions made from this information.…”