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Forest Analytics

  • February 18, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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forest analytics


We all know that forests cover approximately 30% 0f the world's land area and are the dominant terrestrial ecosystem on Earth. In that, we have a variety of species of plants, animals, birds, mammals, etc. Everybody is dependent on forests one or another. In the Northern hemisphere, these forests called taiga or boreal forest, have prolonged winters and between 250 and 500mm (10 and 20 inches) of rainfall annually forests also cover mountains in many temperature parts of the world.

Soil conditions are distinguished according to depth, fertility, and the pressure of perennial roots. Soil depth is important because it determines the extent to which roots can penetrate the earth and, therefore the amount of water and nutrients available to the trees.

Animals that live in forests have highly developed hearing, and many are developed hearing, and many are adapted for vertical movement through the Environment in temperature forests, birds distribute plant seeds and inserts aid in pollination, along with the wind. In tropical forests, fruit bats and birds affect pollination and seed dispersal.

A major contributor to tropical deforestation is the practice of slash-and-burn agriculture and forest small scale farmers clear forests by burning them and then grow crops in the soils fertilized by the ashes. Forest ecosystems have historically received much attention from scientists who have been trying to understand the complex interactions between the various forest analytics processes that drive the dynamics of the system. The recent increase in the availability of a large amount of data and the development of data analysis methods capable of large datasets are providing new opportunities to study these complex systems.

Forest Analytics combines practical, down-to-earth forestry data analysis and solutions to natural forest management challenges with state-of-the-art statistical and data-handling functionality.

Forestry has offered a fertile environment for data analysts to operate since forest measurements began forestry datasets are typically voluminous, hierarchical, messy, multi-faceted, and expensive to do something more. A model or dataset that perfectly suits one application may be inappropriate for another. The same model or datasets may be just the best that can be done at the time and reluctantly accepted. The analyst must be pragmatic. This need for pragmatism must cut across received from statistics and other fields.

Big Data’s going mainstream and at the same, it's rewriting the rules of forestry management. Tree farmers, logging assets can use Big Data analytics to dig up all kinds of insights that can help them achieve their goals of sustainability.

For years, it's been used to forecast the impact of controlled burns, harvests, and other forest management strategies. But until recently it's been a slow and cumbersome process, involving thousands of man-hours spent pouring over custom-made spreadsheets, with a lot of guesswork thrown in.

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Machine Learning in Forest Analytics:

Machine learning, an important branch of artificial intelligence, is increasingly being applied in science such as forest analytics. We discuss three commonly used methods of machine learning including decision-tree Learning, artificial neural network, and support vector machine, and their application in four different aspects of forest analytics.

These applications include (i) a species distribution model (ii)carbon cycle (iii) prediction and (iv)other applications in forest management Although ML approaches are useful for classification, modeling, and prediction in forest analytics research, further expansion of ML technologies, However, the combined algorithms and improved communication and cooperation between forest researchers and ML developers still present major challenges and tasks for the betterment of future forest researchers.

We can suggest that future applications of ML in forests will become an increasingly attractive tool for forest analytics in the face of “big data” and that forests will gain access to more types of data such as sound and video in the future, possibly opening new avenues of research in forest analytics People accumulate historical experiences and generalize these experiences to speculate on novel problems underlying assumptions or processes may not be known to predict specific outcomes. The “training” and “predicting” methods of ML can correspond to the human “generalization” and “speculation” processes. Just as experience is essential in learning, historical datasets play a decisive role in ML.


Machine Learning is helping in different ways to save the forest and animals. New technologies that have emerged over the past decade enable the utilization of novel data approaches in forest monitoring. At the same time, The requirements for the forest. Indicators of carbon balance biodiversity, and forest health to name a few, have an increasingly important role in forests.

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