Next Article in Journal
Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods
Next Article in Special Issue
Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index
Previous Article in Journal
An Iterative Coarse-to-Fine Sub-Sampling Method for Density Reduction of Terrain Point Clouds
Previous Article in Special Issue
Accurate Measurement of Tropical Forest Canopy Heights and Aboveground Carbon Using Structure From Motion
Open AccessArticle

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

1
GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain
2
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 Ås, Norway
3
Department of Forestry, Lilongwe University of Agriculture and Natural Resources, P.O. Box 219, Lilongwe, Malawi
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 948; https://doi.org/10.3390/rs11080948
Received: 21 February 2019 / Revised: 13 April 2019 / Accepted: 17 April 2019 / Published: 19 April 2019
(This article belongs to the Special Issue UAV Applications in Forestry)
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. View Full-Text
Keywords: NIR camera; RGB camera; side overlap; miombo woodlands NIR camera; RGB camera; side overlap; miombo woodlands
Show Figures

Graphical abstract

MDPI and ACS Style

Domingo, D.; Ørka, H.O.; Næsset, E.; Kachamba, D.; Gobakken, T. Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland. Remote Sens. 2019, 11, 948. https://doi.org/10.3390/rs11080948

AMA Style

Domingo D, Ørka HO, Næsset E, Kachamba D, Gobakken T. Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland. Remote Sensing. 2019; 11(8):948. https://doi.org/10.3390/rs11080948

Chicago/Turabian Style

Domingo, Darío; Ørka, Hans O.; Næsset, Erik; Kachamba, Daud; Gobakken, Terje. 2019. "Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland" Remote Sens. 11, no. 8: 948. https://doi.org/10.3390/rs11080948

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop