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Estimating babassu palm density using automatic palm tree detection with very high spatial resolution satellite images

机译:使用自动棕榈树检测和非常高分辨率的卫星图像估算巴巴苏棕榈密度

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摘要

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.
机译:高空间分辨率图像以及图像处理和对象检测算法是有助于研究森林物种的生物多样性和商业人工林的最新技术。本文旨在通过研究随机分散的本地棕榈树,为使用这些技术提供知识。在这里,我们使用在巴西亚马逊小农场社区拍摄的高分辨率全色GeoEye图像(0.50 m),分析了大型圆形树冠(LCC)棕榈树的自动检测。我们还提出了基于检测结果来估计LCC棕榈树Attalea speciosa(babassu)密度的辅助方法。我们基于数学形态学技术在空旷地区检测棕榈树阴影的基础上,使用了“ Compt-palm”算法,并使用现场方法(即结构普查和地理配准)验证了空间信息。该算法识别出处于生命阶段5和6的个体,并使用提取百分比,分支因子和质量百分比因子来评估其性能。主成分分析表明,所研究物种的结构与其他物种不同。在第6阶段,大约有96%的巴巴苏人被检测到。这些人的细纹比未发现的细纹小得多。反过来,在第5阶段的巴巴苏个体中,有60%被检测到,与未检测到的个体相比,显示出明显不同的总高度和不同数量的叶子。我们对资源可用性的计算表明,6870公顷包含25,015棵成年巴巴苏棕榈树,年潜在杏仁油产量为27.4吨。检测LCC棕榈树并实施辅助现场方法以估计巴巴苏密度是监测该行业资源的重要第一步,这对于巴西经济和成千上万的大规模家庭极为重要。

著录项

  • 来源
    《Journal of Environmental Management》 |2017年第may15期|40-51|共12页
  • 作者单位

    Universidade Federal Rural da Amazonia (UFRA), CP. 917, Belem, Para, 66077-530, Brazil,Universidade Federal do Sul e Sudeste do Para (UNIFESSPA), Folha 31, Quadra 07, Lote Especial Nova Maraba, 68507-590, Maraba, Brazil;

    Institut de Recherche pour le Developpement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean Francois Breton, 34093, Montpellier, France;

    Institut de Recherche pour le Developpement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean Francois Breton, 34093, Montpellier, France;

    Institut de Recherche pour le Developpement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean Francois Breton, 34093, Montpellier, France;

    Universidade Federal Rural da Amazonia (UFRA), CP. 917, Belem, Para, 66077-530, Brazil;

    Institut de Recherche pour le Developpement (IRD), UMR 228 ESPACE DEV, 500, Rue Jean Francois Breton, 34093, Montpellier, France;

    Institut de Recherche pour le Developpement (IRD), 911 Avenue Agropolis BP64501, 34394 Montpellier Cedex 05, France;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Shadow detection; Mathematical morphology; Density estimate; Remote sensing; Brazilian Amazon;

    机译:阴影检测;数学形态密度估算;遥感;巴西亚马逊;

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