title: Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images.
abstract: High spatial resolution images as well as image processing and object detection algorithms are recent technologies that aid the study of biodiversity and commercial plantations of forest species. This paper seeks to contribute knowledge regarding the use of these technologies by studying randomly dispersed native palm tree. Here, we analyze the automatic detection of large circular crown (LCC) palm tree using a high spatial resolution panchromatic GeoEye image (0.50 m) taken on the area of a community of small agricultural farms in the Brazilian Amazon. We also propose auxiliary methods to estimate the density of the LCC palm tree Attalea speciosa (babassu) based on the detection results. We used the "Compt-palm" algorithm based on the detection of palm tree shadows in open areas via mathematical morphology techniques and the spatial information was validated using field methods (i.e. structural census and georeferencing). The algorithm recognized individuals in life stages 5 and 6, and the extraction percentage, branching factor and quality percentage factors were used to evaluate its performance. A principal components analysis showed that the structure of the studied species differs from other species. Approximately 96% of the babassu individuals in stage 6 were detected. These individuals had significantly smaller stipes than the undetected ones. In turn, 60% of the stage 5 babassu individuals were detected, showing significantly a different total height and a different number of leaves from the undetected ones. Our calculations regarding resource availability indicate that 6870 ha contained 25,015 adult babassu palm tree, with an annual potential productivity of 27.4 t of almond oil. The detection of LCC palm tree and the implementation of auxiliary field methods to estimate babassu density is an important first step to monitor this industry resource that is extremely important to the Brazilian economy and thousands of families over a large scale.
authors.affiliation: Universidade Federal Rural da Amazonia (UFRA), CP. 917, Belém, Pará, 66077-530, Brazil; Universidade Federal do Sul e Sudeste do Pará (UNIFESSPA), Folha 31, Quadra 07, Lote Especial, Nova Marabá, 68507-590, Marabá, Brazil. Electronic address: alessiomsag@gmail.com.
authors.forename: Alessio Moreira
authors.initials: AM
authors.lastname: Dos Santos
authors.affiliation: Institut de Recherche pour le Développement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean François Breton, 34093, Montpellier, France. Electronic address: danielle.mitja@ird.fr.
authors.forename: Danielle
authors.initials: D
authors.lastname: Mitja
authors.affiliation: Institut de Recherche pour le Développement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean François Breton, 34093, Montpellier, France. Electronic address: eric.delaitre@ird.fr.
authors.forename: Eric
authors.initials: E
authors.lastname: Delaître
authors.affiliation: Institut de Recherche pour le Développement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean François Breton, 34093, Montpellier, France. Electronic address: laurent.demagistri@ird.fr.
authors.forename: Laurent
authors.initials: L
authors.lastname: Demagistri
authors.affiliation: Universidade Federal Rural da Amazonia (UFRA), CP. 917, Belém, Pará, 66077-530, Brazil. Electronic address: izildinha.miranda@ufra.edu.br.
authors.forename: Izildinha
authors.initials: I
authors.lastname: de Souza Miranda
authors.affiliation: Institut de Recherche pour le Développement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean François Breton, 34093, Montpellier, France. Electronic address: therese.libourel@univ-montp2.fr.
authors.forename: Thérèse
authors.initials: T
authors.lastname: Libourel
authors.affiliation: Institut de Recherche pour le Développement (IRD), 911 Avenue Agropolis BP64501, 34394 Montpellier Cedex 05, France. Electronic address: michel.petit@ird.fr.
authors.forename: Michel
authors.initials: M
authors.lastname: Petit
mesh_terms: D028023:Arecaceae; D044822:Biodiversity; D065928:Forests; D006801:Humans; D063809:Satellite Imagery; D014197:Trees
keywords: Brazilian Amazon; Density estimate; Mathematical morphology; Remote sensing; Shadow detection