Reliable environmental monitoring and evaluation require high-quality maps of land use and land cover. For the Amazon biome, the TerraClass and MapBiomas projects apply different methodologies to create these maps. We evaluated the agreement between land cover and land use maps generated by TerraClass and MapBiomas (Collections 2 and 3) for the Brazilian Amazon biome, from 2004 to 2014. Specifically, we: (1) described both project legends based on the LCCS (Land Cover Classification System); (2) analyzed the differences between their classes; and (3) compared the mapping differences among the Brazilian states that are totally or partially covered by the Amazon biome. We compared the classifications with a per-pixel approach and performed an evaluation based on agreement matrices. The overall agreement between the projects was 87.4% (TerraClass x MapBiomas 2) and 92.0% (TerraClass x MapBiomas 3). We analyzed methodological differences to explain the disagreements in class identification. We conclude that using these maps together without a properly adapted legend is not recommended for the analysis of land use and land cover change. Depending on the application, one mapping system may be more suitable than the other.

The ecosystems of savanna cover $\sim 20\%$ of the Earth’s terrestrial area. Especially in tropical regions, they are rich in biodiversity¹ and water resources² and play an important role in carbon stock due to their content of above and below-ground biomass.³ The Brazilian Savanna, also known as Cerrado, is considered a global hotspot for biodiversity conservation, containing around 4800 endemic species.^1,4 In addition, its water resources feed the three largest watersheds in South America: the Amazon, Prata, and São Francisco. The Cerrado comprises 24% of the Brazilian territory, being the second largest biome in the country. However, despite its ecological relevance, only 8.6% of its native vegetation is in protected areas, i.e., specific regions established to protect biodiversity, water bodies, and other environmental resources.⁵ Approximately 47% of the Cerrado native vegetation have already been converted to other land uses, especially pasture (29%) and annual agriculture (9%).⁶ Moreover, from 2008 to 2019, the deforestation rates in the Cerrado have been higher than in the Amazon biome in 9 of these 12 years interval.⁷ This heavy loss of native vegetation has severe environmental consequences, such as vegetation fragmentation, habitat loss,⁸ and reduction of water yield and carbon stocks.^9,10 In this scenario, accurate mapping of Cerrado vegetation is essential to support policies against deforestation and, consequently, to maintain the provision of ecosystem services, since these maps are crucial to assess biodiversity, improve carbon stock estimation within the biome, and guide conservation policies.

Sugarcane products contribute significantly to the Brazilian economy, generating U.S. $12.2 billion in revenue in 2018. Identifying and monitoring factors that induce yield reduction, such as weed occurrence, is thus imperative. The detection of Bermudagrass in sugarcane crops using remote sensing data, however, is a challenge considering their spectral similarity. To overcome this limitation, this paper aims to explore the potential of texture features derived from images acquired by an optical sensor onboard anunmanned aerial vehicle (UAV) to detect Bermudagrass in sugarcane. Aerial images with a spatial resolution of 2 cm were acquired from a sugarcane field in Brazil. The Green-Red Vegetation Index and several texture metrics derived from the gray-level co-occurrence matrix were calculated to perform an automatic classification using arandom forest algorithm. Adding texture metrics to the classification process improved the overall accuracy from 83.00% to 92.54%, and this improvement was greater considering larger window sizes, since they representeda texture transition between two targets. Production losses induced by Bermudagrass presence reached 12.1 tons × ha⁻¹ in the study site. This study not only demonstrated the capacity of UAV images to overcome the well-known limitation of detecting Bermudagrass in sugarcane crops, but also highlighted the importance of texture for high-accuracy quantification of weed invasion in sugarcane crops.