000079632 001__ 79632
000079632 005__ 20200716101445.0
000079632 0247_ $$2doi$$a10.3390/rs11080948
000079632 0248_ $$2sideral$$a112234
000079632 037__ $$aART-2019-112234
000079632 041__ $$aeng
000079632 100__ $$0(orcid)0000-0002-8362-7559$$aDomingo, D$$uUniversidad de Zaragoza
000079632 245__ $$aEffects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland
000079632 260__ $$c2019
000079632 5060_ $$aAccess copy available to the general public$$fUnrestricted
000079632 5203_ $$aUnmanned 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.
000079632 536__ $$9info:eu-repo/grantAgreement/ES/MEC/FPU14-06250
000079632 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000079632 590__ $$a4.509$$b2019
000079632 591__ $$aREMOTE SENSING$$b9 / 30 = 0.3$$c2019$$dQ2$$eT1
000079632 592__ $$a1.422$$b2019
000079632 593__ $$aEarth and Planetary Sciences (miscellaneous)$$c2019$$dQ1
000079632 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000079632 700__ $$aOrka, H.O
000079632 700__ $$aNaesset, E
000079632 700__ $$aKachamba, D
000079632 700__ $$aGobakken, T.
000079632 7102_ $$13006$$2010$$aUniversidad de Zaragoza$$bDpto. Geograf. Ordenac.Territ.$$cÁrea Análisis Geográfico Regi.
000079632 773__ $$g11, 8 (2019), 948 [17 pp]$$pRemote sens. (Basel)$$tRemote Sensing$$x2072-4292
000079632 8564_ $$s1296692$$uhttps://zaguan.unizar.es/record/79632/files/texto_completo.pdf$$yVersión publicada
000079632 8564_ $$s105701$$uhttps://zaguan.unizar.es/record/79632/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000079632 909CO $$ooai:zaguan.unizar.es:79632$$particulos$$pdriver
000079632 951__ $$a2020-07-16-09:02:26
000079632 980__ $$aARTICLE