New geospatial intelligence methodology makes land use management more accurate and faster

Researchers from São Paulo State University (UNESP), at its Tupã campus in Brazil, have developed and tested a new geospatial intelligence methodology that can contribute more quickly and accurately to land use management and territorial planning projects. With this tool, it was possible to precisely delineate areas of Amazon rainforest, Cerrado vegetation (the Brazilian savannah-like biome), pastures and agricultural crops in a double-cropping system, something that can provide support for public policies aimed at agricultural production and environmental conservation.

By combining data cube architecture (ready for analysis), disseminated in Brazil through the Brazil Data Cube project, led by the National Institute for Space Research (INPE), and the Geobia (Geographic Object-Based Image Analysis) approach, the scientists were able to identify vegetation and double cropping – for example, soy and corn – over the course of a harvest in the state of Mato Grosso. They used time series of satellite images from NASA’s Modis (Moderate Resolution Imaging Spectroradiometer) sensor.

The results showed that the proposed combination, coupled with machine learning (artificial intelligence) algorithms, achieved 95% mapping accuracy.

Geobiology is a technique that allows satellite images to be processed using segmentations that group similar pixels into geo-objects and study their characteristics, such as shape, texture, and reflectance. In many cases, this allows for a more realistic interpretation. Data cubes, on the other hand, store information in dimensions – time and place – making it easier to aggregate and visualize information related to a specific location in a specific time period, such as crop areas in a harvest year.

Currently, mapping uses pixel image analysis in isolation, which ends up creating edge problems with blurring in some areas. “Scientific work has highlighted spectral confusion in border zones between different land uses as an area for improvement. So we decided to segment the images and evaluate the geographical object as the minimum unit of analysis, rather than the pixel. It’s as if the image were broken down and classified according to each piece. In this way, we were able to reduce recurring edge errors and accurately identify the targets, even with moderate spatial resolution,” Michel Eustáquio Dantas Chaves, professor at the Faculty of Science and Engineering of UNESP and corresponding author of the article, told Agência FAPESP.

Chaves has been using data cube architecture for several years to develop tools that contribute to analyses focused on the advancement of the agricultural frontier, especially in the Cerrado.

According to the professor, the methodology can be replicated to evaluate images from other Earth observation satellites, such as Landsat and Sentinel, which provide data for scientific studies, mapping and monitoring. Images from both are now being processed by the team coordinated by the professor.

The article describing the methodology was published in the special issue Research Progress and Challenges of Agricultural Information Technology of the scientific journal AgriEngineering. The study was supported by FAPESP through three projects (21/07382-2, 23/09903-5 and 24/08083-7).

Application in practice

Mato Grosso leads national grain production with 31.4% of the country’s total, followed by the states of Paraná (12.8%) and Rio Grande do Sul (11.8%). The state is expected to reach 97.3 million tons in the 2024/2025 harvest, an increase of 4.4% over the previous harvest, according to the National Supply Company (CONAB). Almost half of this production (46.1 million tons) is expected to be soybeans.

In addition, Mato Grosso is one of the most biodiverse states in the country, containing parts of three of Brazil’s six biomes. Around 53% of its territory is in the Amazon, 40% in the Cerrado and 7% in the Pantanal.

Due to this heterogeneity of land uses and vegetation types in the territory, the researchers applied the new methodology in Mato Grosso using data from the 2016/2017 strategic harvest, in which Brazil produced 115 million tons of soybeans, of which 30.7 million tons were in the state. Land use classifications were associated with agricultural land (fallow-cotton, soybean-cotton, soybean-corn, soybean-fallow, soybean-millet and soybean-sunflower), as well as sugarcane crops, urban areas and water bodies.

The results showed an overall accuracy of 95%, demonstrating the potential of the approach to provide mapping that optimizes forest and agricultural land delineation. “Since the approach manages to identify the targets in a consistent manner, the methodology can be applied to the estimation of areas within the same harvest, favoring productivity estimates; in territorial planning actions and anything that deals with land use and land cover for decision-making,” explains Chaves about the application of the tool.

The professor explains that the methodology also makes it possible to analyze disturbances in forests and other types of natural vegetation. “It’s quicker to detect deforestation than degradation. This method allowed us to detect these variations more quickly.”

In the article, the scientists pay tribute to Professor Ieda Del’Arco Sanches, a remote sensing researcher at INPE who died in January. “This article is a way of thanking her for her teachings and following her legacy. Ieda always worked to accurately assess the Earth’s surface and to treat the data ethically and responsibly, showing how they can contribute to the construction of public policies,” adds Chaves.

About FAPESP

The São Paulo Research Foundation (FAPESP) is a public institution with the mission of supporting scientific research in all fields of knowledge by awarding scholarships, fellowships and grants to investigators linked with higher education and research institutions in the state of São Paulo, Brazil. FAPESP is aware that the very best research can only be done by working with the best researchers internationally. Therefore, it has established partnerships with funding agencies, higher education, private companies, and research organizations in other countries known for the quality of their research and has been encouraging scientists funded by its grants to further develop their international collaboration.