Tree Species Classification from Aerial Images and LIDAR in Agricultural Areas

8/13/2019 Tree Species Classification from Aerial Images and LIDAR in Agricultural Areas

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TREE SPECIES CLASSIFICATION FROM AERIAL IMAGES AND LIDAR IN

AGRICULTURAL AREAS

Antonio Ruiz1, Oriol Vias1, Albert Domingo2and Valent Marco21Institut Cartogric de Catalun!a2De"artament d#Agricultura, Alimentaci$ i Acci$ Rural

%e "resent t&e results o a test stud! on tree s"ecies classiication in an agricultural area rom digitalaerial images and lidar' (&e ob)ecti*e +as to distinguis& bet+een t&ree s"ecies t&at are armed in t&e areaoli*e tree, almond tree and carob tree- in order to obtain an eicient tec&ni.ue or re*ie+ing andu"dating t&e /0I /and 0arcel Inormation !stem- establis&ed b! Council regulation C- 134252667to c&ec8 area9based subsidies on agriculture: ot&er s"ecies t&at are "resent +ere not considered' Digitalaerial "&otogra"&s +it& 2; cm "i


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"anc&romatic c&annel and to 32 cm o t&e our lo+9resolution s"ectral c&annels@ red, green, blue and nearinrared'

Brom DMC data t+o 8inds o images +ere created Bigure 2- combining 7 o t&e = colour c&annelsgenerating t&e true colour ile c&annels ?lue, >reen and Red- and t&e IRC colour ile c&annels >reen,Red and Inrared-

Bigure 2' (rue colour VI- images on to" and IRC images on bottom' %inter images on t&e let andsummer images on t&e rig&t' On to" let image also t&e training trees are s&o+n@ almond trees in blue,carob in green and oli*e in !ello+' '

Ater aerial triangulation, DMC images +ere resam"led to 6'2;, 6'; and 1 m "i


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dierent e"oc&s /ee et al', 266-' (&e "roblem +as sol*ed recti!ing t&e images +it& a DM includingtree models +&ere eac& tree +as re"resented as a generalized c!linder (see details further on). (&eresulting image is not beautiul because blac8 areas a""ear ne05R(H in 7 tests areas +it& dierent co*erage' (&e ele*ation o t&e "oints +ascom"ared to t&e ele*ation o a (I model built rom lidar "oints classiied as ground bare9eart& (Imodel-' (&e results o t&e c&ec8ing +it& ground trut& are s&o+n in (able 2@

Area 1 Area 2 Area 3 TotalCoera!e Oli*e trees Vin!ard (ennis courtA""ro


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binomial 7


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Big' /et, 74 ield measured trees' Rig&t@ 71 stands +it& &omogeneous cro"s'Oli*e trees, !ello+: carob, green and almond, "ur"le'

Co"#ari$on o% lidar deried and %ield "ea$ured #ara"eter$

A com"arison o ield measured trees +it& lidar deri*ed "arameters +as done' (rees +ere identiied int&e CFM image ater t&eir coordinates' Bield measured tree &eig&ts +ere com"ared +it& lidar &eig&tsdirectl!' Bigure 3a s&o+s t&at lidar deri*ed &eig&ts are in good agreement +it& ield9measured *alues butdis"ersion is &ig&er or almond trees' (&is is a deciduous tree and, at t&e time o t&e lidar data ca"ture,t&e! &ad no lea*es' Des"ite t&at, almonds +ere detected +it& lidar data onl!'

Cro+n area +as not directl! measured on t&e ield but +e can estimate it assuming t&at t&e s&a"e o t&ecro+n "ro)ected on t&e ground is an elli"sis and t&e ma


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(able 7' egmentation "arameters

0i


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A series o eCognition trials +it& dierent selections o *ariables +as carried out to "erorm ane


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Bigure ' On t&e let, color inrared images' On t&e rig&t, classiication results corres"onding to 166 cmresolution, multitem"oral data and = s"ectral c&annels' Oli*es in !ello+, carob in green, almond in

"ur"le, orest trees and "o+erlines in red, grass and ground in gre!'

Oli*e Carob Almond(otalOli*e 7 2-1 73- 1= 76- 127 9-Carob 3 3-2; ;4- 2 =- 7= 9-

Almond 1 1- 2 ;- 71 - 7= 9-(otal 161 9- =7 9- =3 9- 11 34-1. lidar)deried #ara"eter$

Oli*e Carob Almond(otalOli*e 6 4-1 73- 1= 76- 126 9-Carob 4 4-2; ;4- 1 2- 7= 9-

Almond 7 7- 2 ;- 72 4- 73 9-(otal 161 9- =7 9- =3 9- 11 33-1,)*e$t lidar)deried #ara"eter$

(able ; Contingenc! tables or lidar9deri*ed "arameters "ercentages into "arent&eses-'

(&e classiication "erormance im"ro*ed +&en multitem"oral images rom DMC +ere incor"orated tot&e lidar data 16 lidar *ariables-,, es"eciall! +it& t&e combination o inrared, green and red c&annels oeac& e"oc& (able -' (&e im"ro*ement is larger or carob rom ;4G to 3=G- and almond trees rom4G to 4G- and smaller or oli*e trees rom 4G to 1G- because t&e good results ac&ie*ed +it& lidardata in t&is case let little room or additional im"ro*ement'

Oli*e Carob Almond(otalOli*e 2 1-11 2-1 2- 16= 9-Carob - 72 3=- 6 6- =1 9-Almond6 6- 6 6- = 4- = 9-(otal 161 9- =7 9- =3 9- 11 4-(able ' Contingenc! table or t&e 169bestlidar9deri*ed "arameters "lus I, R, >multitem"oral c&annels o t&e DMC'DMC data &as also been anal!sed se"aratel! o lidar' In t&e ollo+ing tests, onl! t&e lidar CFM image&as been em"lo!ed during t&e segmentation "&ase' Regarding t&e "i


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&as not reduced t&e original resolution o t&e s"ectral c&annels and, on t&e o""osite, using &ig&l! "an9s&ar"ened resolutions 2;9;6 cm- ma! degrade t&e s"ectral inormation'

(able 3' Multitem"oral DMC data at dierent "i?- and IRC Oli*e Carob Almond(otalOli*e 34 3- 13 =6- 1 2- Carob 14 14-2= ;3-6 6- =2Almond 7 7- 1 2- = 4- ;6(otal =2 =3 144 3-DMC 1,, c" 1$te#oc&- RG0

Oli*e Carob Almond (otalOli*e 4; 4- 17 71- 6 6- 4Carob 1= 1=- 2 - 6 6- =7Almond6 6- 6 6- =3 166-=3(otal =2 =3 144 4-DMC 1,, c" 1$te#oc&- IRC

Oli*e Carob Almond(otalOli*e 2 1-14 =2-7 - 117Carob 3 3- 14 =2- 17- 71

Almond2 2- 3 1- 74 41- =3(otal 161 =7 =3 11 33-DMC 1,, c" 2nde#oc&- RG0

Oli*e Carob Almond(otalOli*e 47 42-2= ;3-7 - 116Carob 11 11-12 2- 17- 2

Almond3 3- 1=- 74 41- ;1(otal 161 =2 =3 16 36-DMC 1,, c" 2nde#oc&- IRC

Oli*e Carob Almond(otalOli*e 4 6-1; 7-2 =- 16Carob 4 4- 2; 6-6 6- 77Almond 2 2- 2 ;- =; - =(otal =2 =3 144 4;-DMC 1,, c" *ot& e#oc&$- RG0

Oli*e Carob Almond (otalOli*e = 7- 11 2- 6 6- 16;Carob 3 3- 72 3=-6 6- 7Almond6 6- 6 6- =3 166-=3(otal 161 =7 =3 11 1-DMC 1,, c" *ot& e#oc&$- IRC

Results rom onl! one e"oc& +ere "oorer or oli*e trees t&an t&ose obtained rom multitem"oral imagestables 3 E - but +inter images &a*e been critical to discriminate almond trees'

(able ' Contingenc! tables or DMC all c&annels altoget&er-Oli*e Carob Almond (otal

Oli*e 47 4=- 21- 6 6- 2Carob 1 1- 77 3- 6 6- =

Almond 6 6- 6 6- =3 166- =3(otal =2 =3 144 43-

DMC 1,, c" 1$te#oc&

Oli*e Carob Almond (otalOli*e 4; 4=-14 =2- ; 11- 164Carob 3 3- 21 =- ; 11- 77

Almond - = - 73 3- ;6(otal 161 =7 =3 11 3;-

DMC 1,, c" 2nde#oc&


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Conclu$ion$

(&e &eig&t and cro+n area deri*ed rom lidar are in good agreement +it& ield9measured *alues ta8inginto account t&at t&e lidar "oint densit! t&at +as em"lo!ed in t&is stud! +as lo+'

(a8ing into account t&e 1; lidar9deri*ed "arameters, oli*e trees +ere correctl! identiied in 2G o t&e

cases but or t&e ot&er s"ecies t&e success rate +as less t&an 36G' (&e discrimination bet+een s"eciesac&ie*ed a maXell, %olgang Hornus, M!riam Mo!sset, Ramon

AlamYs, oan Mart, Da*id (orrents, ordi FernZndez, Daniel Barr[, os[ Antonio Ortega, Vicen\ 0al,Anna (ard, Bernando 0[rez, udit BernZndez, Mi.uel ]ngel Ortiz, Roman Arbiol, uli (ala!a and^a*ier ste*e'

Re%erence$

C&ube!, M', Bran8lin '', and %ulder, M'A' Ob)ect9based anal!sis o I8onos92 imager! or e


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aesset, ' and ?)er8nes, H'O' stimating tree &eig&ts and number o stems in !oung orest stands usingairborne laser scanner data' Remote ensing o n*ironment2661:34, 72497=6'

0al, V', Arbiol, R', 0[rez, B' >eneraci$n de ortoimZgenes en Zreas urbanas' Re*ista de (eledetecci$n,2661: 1@ =39;6'

0al, V', Arbiol, R' (rue ort&oimage generation in urban areas' 0roc' o t&e 7rd International !m"osiumRemote ensing o rban Areas' Istambul, 2662: 1@ 76971='

0o"escu, 'C', and Randol"& F' %!nne' eeing t&e trees in t&e orest@ using lidar and multis"ectral datausion +it& local iltering and *ariable +indo+ size or estimating tree &eig&t' 0R 266=: 36@;496='

Viau, A'A, ang, '9D', 0a!an, V' and A' De*ost' (&e use o airborne /idar ans multis"ectral sensors ororc&ard tree in*entor! and c&aracterization' 3 t& Bruit, ut and Vegetable 0roduction ngineering!m"osium BR(IC 6;N, Mont"ellier, 266;' "ag'494'

Vias, O', Ruiz, A', ^andri, R', 0al, V', Arbiol, R'Combined use o /idar and `uic8bird data or t&egeneration o land use ma"s I0R Mid9term !s"osium Brom 0i

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Tree Species Classification from Aerial Images and LIDAR in Agricultural Areas