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.

Cross-habitat spillover may be the outcome of a process of habitat loss or degradation where the receiving habitat serves as a refuge for organisms. Once surface habitats are lost or degraded, animals can find underground refuge in caves. This paper is focused on testing whether taxonomic order richness inside caves is positively affected by the loss of the native vegetation cover surrounding caves; whether degradation of native vegetation cover predicts cave community composition; and whether there is a pattern of cave community clusters delimited by similarity in the effects of habitat degradation on animal communities. We gathered a comprehensive speleological dataset consisting of occurrence data of thousands of invertebrates and vertebrates sampled in 864 iron caves in the Amazon, to test the effects of both variables measured inside caves and surrounding landscapes on spatial variation in richness and composition of animal communities. We show that caves can work as refuges for the fauna in landscapes where the native vegetation cover surrounding them was degraded, which was evidenced by landcover change increasing the richness of cave communities and clustering caves by similarity in community composition. Therefore, habitat degradation on the surface should be a key variable when characterizing cave ecosystems for conservation prioritization and offset planning. Habitat degradation causing a cross-habitat spillover effect highlights the importance of maintaining the connection between caves by the surface, especially large caves. Our study can help guide industry and stakeholders working on the complex conciliation between land use and biodiversity conservation.