Planning Harvesting Operations in Forest Environment: Remote Sensing for Decision Support

Piragnolo, Marco; Grigolato, Stefano; Pirotti, Francesco

doi:10.5194/isprs-annals-iv-3-w1-33-2019

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…Digital FIs at present are generally deployed to guide machines through optimal paths to follow. These are elaborated according to predefined criteria, such as minimum cost, minimum impact on soil or standing trees, or a compromise among these (Piragnolo et al 2019). In optimal cases, where geopositioning is highly reliable and accurate, this application can be stretched to the level of a single tree to cut, implementing a virtual tree marking performed directly on the digital inventory.…”

Section: Sensors On Forest Machinesmentioning

confidence: 99%

Smart Harvest Operations and Timber Processing for Improved Forest Management

Picchi

Sandak²,

Grigolato

et al. 2021

Managing Forest Ecosystems

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Climate-smart forestry can be regarded as the evolution of traditional silviculture. As such, it must rely on smart harvesting equipment and techniques for a reliable and effective application. The introduction of sensors and digital information technologies in forest inventories, operation planning, and work execution enables the achievement of the desired results and provides a range of additional opportunities and data. The latter may help to better understand the results of management options on forest health, timber quality, and many other applications. The introduction of intelligent forest machines may multiply the beneficial effect of digital data gathered for forest monitoring and management, resulting in forest harvesting operations being more sustainable in terms of costs and environment. The interaction can be pushed even further by including the timber processing industry, which assesses physical and chemical characteristics of wood with sensors to optimize the transformation process. With the support of an item-level traceability system, the same data could provide a formidable contribution to CSF. The “memory” of wood could support scientists to understand the response of trees to climate-induced stresses and to design accordingly an adaptive silviculture, contributing to forest resilience in the face of future changes due to human-induced climate alteration.

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Section: Sensors On Forest Machinesmentioning

confidence: 99%

Smart Harvest Operations and Timber Processing for Improved Forest Management

Picchi

Sandak²,

Grigolato

et al. 2021

Managing Forest Ecosystems

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“…Finally the classification is performed using a classifier (RF, ANN, TF… etc). The multitude of applications for machine learning approaches for spatial data is shown in the recent numerous publications that focus on using these algorithms for image analysis (Pirotti et al, 2016) and also support raster-based spatial predictions (Piragnolo et al, 2019). In this work we compared point clouds classification accuracy of Random Forest and Tensorflow.…”

mentioning

confidence: 99%

Classification of Aerial Laser Scanning Point Clouds Using Machine Learning: A Comparison Between Random Forest and Tensorflow

Pirotti

Tonion

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> In this investigation a comparison between two machine learning (ML) models for semantic classification of an aerial laser scanner point cloud is presented. One model is Random Forest (RF), the other is a multi-layer neural network, TensorFlow (TF). Accuracy results were compared over a growing set of training data, using a stratified independent sampling over classes from 5% to 50% of the total dataset. Results show RF to have average F1&thinsp;=&thinsp;0.823 for the 9 classes considered, whereas TF had average F1&thinsp;=&thinsp;0.450. F1 values where higher for RF than TF, due to complexity in the determination of a suitable composition of the hidden layers of the neural network in TF, and this can likely be improved to reach higher accuracy values. Further study in this sense is planned.</p>

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Ormancılıkta makine öğrenmesi kullanımı

Eker

Alkiş

Ucar

et al. 2023

Turkish Journal of Forestry | Türkiye Ormancılık Dergisi

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Gelişen teknolojiyle beraber diğer disiplinlerde olduğu gibi ormancılıkta da geleneksel uygulamaların daha ekonomik, etkin, hızlı ve kolay yapılabilmesi için yenilikçi yaklaşımların kullanımına talepler ve ihtiyaçlar artmaktadır. Özellikle son dönemde ortaya çıkan ormancılık bilişimi, hassas ormancılık, akıllı ormancılık, Ormancılık (Forestry) 4.0, iklim-akıllı ormancılık, sayısal ormancılık ve ormancılık büyük verisi gibi terimler ormancılık disiplinin gündeminde yer almaya başlamıştır. Bunların neticesinde de makine öğrenmesi ve son dönemde ortaya çıkan otomatik makine öğrenmesi (AutoML) gibi modern yaklaşımların ormancılıkta karar verme süreçlerine entegre edildiği akademik çalışmaların sayısında önemli artışlar gözlenmektedir. Bu çalışma, makine öğrenmesi algoritmalarının Türkçe dilinde anlaşılırlığını daha da artırmak, yaygınlaştırmak ve ilgilenen araştırmacılar için ormancılıkta kullanımına yönelik bir kaynak olarak değerlendirilmesi amacıyla ortaya konulmuştur. Böylece çeşitli ormancılık faaliyetlerinde makine öğrenmesinin hem geçmişten günümüze nasıl kullanıldığını hem de gelecekte kullanım potansiyelini ortaya koyan bir derleme makalesinin ulusal literatüre kazandırılması amaçlanmıştır.

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Planning Harvesting Operations in Forest Environment: Remote Sensing for Decision Support

Cited by 5 publications

References 29 publications

Smart Harvest Operations and Timber Processing for Improved Forest Management

Smart Harvest Operations and Timber Processing for Improved Forest Management

Classification of Aerial Laser Scanning Point Clouds Using Machine Learning: A Comparison Between Random Forest and Tensorflow

Ormancılıkta makine öğrenmesi kullanımı

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