Treedetection: Automatic Tree Detection Using Uav-Based Data

Hentz, Ângela Maria Klein; Corte, Ana Paula Dalla; Netto, Sylvio Péllico; Strager, Michael P.; Schoeninger, Emerson Roberto

doi:10.5380/rf.v48i3.56150

Cited by 14 publications

(13 citation statements)

References 18 publications

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“…The newly developed Unmanned Aerial Vehicles (UAV) complement the established remote sensing (RS) methods. UAVs have increased the number of benefits, such as acquiring extreme high-spatial resolution data, flexibility in usage and over time, and the capacity to carry various sensors, such as a multispectral camera, to observe vegetation health [25,26].…”

Section: Introductionmentioning

confidence: 99%

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

et al. 2021

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Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.

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Section: Introductionmentioning

confidence: 99%

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

et al. 2021

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“…The same limitation occurs with hyperspectral-based methods, such as [13], which uses a hyperspectral frame format camera and an RGB camera along with 3D modelling and Multilayer Perceptron (MLP) neural networks, and obtains accuracy values ranging from 40% to 95% depending on the conditions of the area. Following the idea of exploiting the 3D-shape of trees, some methods perform tree detection from RGB images using generated Digital Surface Models (DSMs), Structure-from-Motion (SfM) or local-maxima based algorithms on UAV-derived Canopy Height Models (CHMs) [14,15]. Nevertheless, the aforementioned methods are likely to show poor performance for trees with irregular canopy, trees in mixed-species forests, or trees that are partially occluded by taller trees.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Morales

Kemper

Sevillano

et al. 2018

Forests

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One of the most important ecosystems in the Amazon rainforest is the Mauritia flexuosa swamp or “aguajal”. However, deforestation of its dominant species, the Mauritia flexuosa palm, also known as “aguaje”, is a common issue, and conservation is poorly monitored because of the difficult access to these swamps. The contribution of this paper is twofold: the presentation of a dataset called MauFlex, and the proposal of a segmentation and measurement method for areas covered in Mauritia flexuosa palms using high-resolution aerial images acquired by UAVs. The method performs a semantic segmentation of Mauritia flexuosa using an end-to-end trainable Convolutional Neural Network (CNN) based on the Deeplab v3+ architecture. Images were acquired under different environment and light conditions using three different RGB cameras. The MauFlex dataset was created from these images and it consists of 25,248 image patches of 512 × 512 pixels and their respective ground truth masks. The results over the test set achieved an accuracy of 98.143%, specificity of 96.599%, and sensitivity of 95.556%. It is shown that our method is able not only to detect full-grown isolated Mauritia flexuosa palms, but also young palms or palms partially covered by other types of vegetation.

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“…Foram testados dois tratamentos, sendo: a) processamento com algoritmo do software LIDAR 360 (GREENVALLEY INTERNATIONAL, 2018) para plantios jovens (Tj1) e plantios adultos (Ta1); e b) processamento no software ArcGis com a ferramenta TreeDetect (HENTZ et al, 2018), também para plantios jovens (Tj2) e plantios adultos (Ta2). A determinação da qualidade do processamento de cada 367 tratamento se deu com a comparação dos resultados com a contagem manual em tela, sendo Tj0 o tratamento testemunha plantios jovens e Ta0 o tratamento testemunha plantios adultos.…”

Section: Detecção E Contagem Dos Alvosunclassified

“…Para tanto foram criados raster das áreas com plantios jovens e adultos pela intersecção com os shapes de interesse. Nesse processamento, utilizou-se a ferramenta TreeDetect, que consiste numa ferramenta, em linguagem Python, construída em ambiente ArcGis (HENTZ et al, 2018). Os parâmetros adotados para a detecção das árvores foram:…”

Section: Treedetectunclassified

Desempenho De Algoritmos Para Contagem Automática De Árvores Jovens E Adultas a Partir De Dados Vant

Silva¹,

Côrte²,

Piva³

et al. 2018

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RESUMO Esse trabalho objetivou testar o desempenho do software LiDAR360 e da ferramenta TreeDetect para detecção e contagem automática de árvores adultas e jovens, através de produtos gerados a partir de imagens VANT. A área de estudo, que localiza-se em Telêmaco Borba-PR, consiste de plantios jovens de Eucalyptus grandis x Eucalyptus urophylla e plantios adultos de Eucalyptus spp., com espaçamento de 3,75 x 2,4 m. Utilizaram-se 20 fotografias aéreas coletadas em setembro de 2015 com VANT eBee-Ag equipado com câmera RGB Canon S110. Realizou-se o pré-processamento no software Agisoft PhotoScan Professional, visando a geração do ortomosaico e nuvem de pontos. Primeiro avaliou-se a eficiência dos algoritmos em plantios jovens: LIDAR360 (Tj1) e TreeDetect (Tj2). Num segundo momento avaliou-se em plantios adultos: LIDAR360 (Ta1) e TreeDetect (Ta2). Para comparação, usou-se a testemunha jovem (Tj0) e a testemunha adulta (Ta0), ambos resultantes de contagem em tela. Considerou-se na análise três parâmetros: (a) indivíduos corretamente identificados, (b) erros de omissão e (c) erros de comissão. Para verificar a ocorrência de diferença estatística entre os tratamentos com relação às testemunhas, aplicou-se o teste t pareado, a um nível de 5% de probabilidade. Observou-se a identificação de 25,83% em Tj1 e 104,54% em Tj2 em relação a Tj0. Nos adultos a identificação foi de 68,44% em Ta1 e de 119,58% em Ta2, em relação a Ta0. Em todos os casos, os tratamentos não diferiram estatisticamente das testemunhas. Recomenda-se testar os algoritmos em outras áreas e em fotografias com melhor qualidade. PALAVRAS-CHAVE: automação de inventário, nuvem de pontos, ortomosaico.

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Treedetection: Automatic Tree Detection Using Uav-Based Data

Cited by 14 publications

References 18 publications

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV)

Automatic Segmentation of Mauritia flexuosa in Unmanned Aerial Vehicle (UAV) Imagery Using Deep Learning

Desempenho De Algoritmos Para Contagem Automática De Árvores Jovens E Adultas a Partir De Dados Vant

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