Estimation of Mango Tree Count and Crown Cover Delineation using Template Matching Algorithm

Abstract

Light detection and Ranging (LiDAR) nowadays is becoming a widely used tool in forest inventory management. LiDAR data has three dimensional structure information that can be used to estimate tree height and crown diameter, the use of this available LiDAR information, availability of image processing software and well developed algorithm allow us to estimate and detect individual tree counts. Several algorithms were developed for different types of remote sensing images, and were tested in different forest areas. However, there were very few studies testing an algorithm in tree crop plantations due to the problems in detecting individual trees affected by varying conditions such as different size of canopy of young and old trees, clumped or clustered trees which sometimes lead to underestimation. This paper aims to estimate the number of trees using LiDAR data and high resolution imagery using a single detection method. The study was conducted in Mangifera indica plantation. The study area is composed of young and matured mango trees having different tree crown size, the planting orientation of mango in the area also vary from isolated and clustered mango tree crown which is a challenge in detecting individual trees. Template matching method in eCognition Developer 9 was applied to different layers derived from point cloud data and high resolution image. Canopy Height Model (CHM) was found to be the optimal layer in creating template sample based on the correlation value of 0.94. The results demonstrated that template matching algorithm can detect the mango tree crowns with 88 % accuracy based on the reference data which is manually delineated. This study suggests that template matching algorithm can be used to estimate mango tree counts. Further study is needed to further improve the algorithm to increase the percent detection. The study also suggested that the method be tested in other types of plantation.

Template matching technique for smart tree detection and counting with UAV imagery

“…The final accuracies obtained in this study are very satisfactory and similar to previous research that used drone images for individual tree detection (Figure 6ljjúp0), which is one of the most challenging problems in precision agriculture and remote sensing. Miguelito [8]applied template matching to count and estimate mango trees crowns. He successfully detected 1272 out of the total reference data of 1473 with a precision of 88%.…”

Análise de eficiência do uso de algoritmo de correspondência de modelo para contagem de plantas

“…Já dentro no software eCognition Developer usando o algoritmo de correspondência de modelo que é baseado na elaboração de modelos genéricos que buscam nas imagens os objetos com características comuns, permitindo a contagem de objetos de forma automática (Norzaki & Tahar, 2019;Isip et al, 2018), vários testes de contagem de plantas foram realizados usando as bandas vermelha, verde e azul e a banda do vermelho com uma modificação em seu histograma pela técnica de realce linear, cuja foi definido valores mínimos e máximos de cortes.…”

“…Rex et al (2018) conseguiu um acerto de 88,4 % na identificação de copas de Araucaria angustifolia em uma área densa de vegetação nativa e Norzaki e Tahar (2019) de 95,3% de precisão na contagem de árvores. Por último, em uma contagem de mangueiras, uma precisão de 88,89 % foi obtida por Isip et al (2018), com copas espaçadas. Research, Society and Development, v. 9, n. 7, e668974576, 2020 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v9i7.4576…”