Generation of forest inventories with a portfolio of remote sensing solutions for Brazilian pulp and paper companies

• Grant number: 17/25639-5
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE
• Start date: August 01, 2018
• End date: July 31, 2019
• Field of knowledge: Agronomical Sciences - Forestry Resources and Forestry Engineering - Forest Management
• Agreement: National Research Council of Canada
• Principal Investigator: Cleber Gonzales de Oliveira
• Grantee: Cleber Gonzales de Oliveira
• Principal researcher abroad: Bruce Mcarthur
• Institution abroad: National Research Council Canada (NRC) , Canada
• CNAE: Produção florestal - florestas plantadas ; Atividades de apoio à produção florestal

Abstract

Brazil and Latin America have an important forestry sector, especially in the paper and pulp sector. The inventory is a basic stage of forest management in which the composition of the forest and its potential for management are evaluated (Araújo, 2006). Forest inventories are needed for the Brazilian pulp and paper industry and as orbital remote sensing data is becoming increasingly available, cheap and collected more frequently; one of the value-added products that can be offered to the forest sector is inventories based in orbital remote sensing data. Today, these inventories are produced with data obtained from field plots that are distributed in large forest areas with densities ranging from 3 to 10 hectares per plot, which makes them intensive in labor demand, expensive and inefficient. It is estimated that the costs of these installments are rarely less than R $ 100.00. Therefore, for this hypothetical area forestry companies can spend about $ 2 million just for data collection. In addition, large forest companies have departments dedicated exclusively to the creation, handling and interpretation of these inventories. Large Brazilian companies can spend about R $ 15 million annually on forest inventories. Tesera is a Canadian company that specializes in using machine-learning techniques in remote sensing data and field plot data to predict variables of forest interest, creating operational forest inventories for the forest industry and also for the Canadian government. In Canada, Tesera predominantly uses a combination of airborne LiDAR data and optical data collected by aircraft or Earth observation satellites. While Visiona offers a variety of orbital remote sensing data that includes optical images and different radar bands (X, C, and L), none of the companies have the know-how to scale an inventory solution that meets customer specifications, with price and mainly suitable accuracy. In this project, Visiona will involve existing and new customers in the pulp and paper industry, which will provide forest data commonly obtained in field plots in areas for which Visiona will obtain layers of data from the chosen satellites, including optical images and data of several radar bands. The minimum area of each experimental unit to be evaluated will correspond to 10,000 hectares. Tesera will compile and analyze the data, creating predictive models for each data combination and experimental unit. During the project, the companies will jointly seek a technical solution to use the Tesera viewer and display the inventories to customers via the internet. The ultimate goal will be to develop a portfolio of inventory solutions that, by combining the correct remote sensing data, will respect customer specifications for a range of factors such as price and expected performance and that can be stored, updated and infrastructure developed in the project. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

FERREIRA DE SOUZA DINIZ, JULIANA MARIA; GAMA, FABIO FURLAN; DOS REIS, ALINY APARECIDA; DE OLIVEIRA, CLEBER GONZALES; GIRARDI MARQUES, EDUARDO RESENDE. Estimating stem volume of Eucalyptus sp. and Pinus sp. plantations in Brazil, using Sentinel-1B and ALOS-2/PALSAR-2 data. JOURNAL OF APPLIED REMOTE SENSING, v. 17, n. 1, p. 20-pg., 2023-01-01. (17/25639-5)