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The delineation of management zones is one of the ways to enable the spatially differentiated management of plots using precision agriculture tools. Over the years, the spatial variability of data collected from soil and plant sampling started to be replaced by data collected by proximal and orbital sensors. As a result, the variety and volume of data have increased considerably, making it necessary to use advanced computational tools, such as machine learning, for data analysis and decision-making support. This paper presents a methodology used to establish management zones (MZ) in precision agriculture by analyzing data obtained from soil sampling, proximal sensors and orbital sensors, in experiments carried out in four plots featuring soybean–cotton and soybean–corn crops, in Mato Grosso and Paraná states, Brazil. Four procedures were evaluated, using different input data sets for the MZ delineation: (I) soil attributes, including clay content, apparent electrical conductivity or fertility, along with elevation, yield maps and vegetation indices (VIs) captured during the peak crop biomass period; (II) soil attributes in conjunction with VIs demonstrating strong correlations; (III) solely VIs exhibiting robust correlation with soil attributes and yield; (IV) VIs selected via random forests to identify the importance of the variable for estimating yield. The results showed that the VIs derived from satellite images could effectively replace other types of data. For plots where the natural spatial variability can be easily identified, all procedures favor obtaining MZ maps that allow reductions of 40% to 70% in yield variance, justifying their use. On the other hand, in plots with low natural spatial variability and that do not have reliable yield maps, different data sets used as input do not help in obtaining feasible MZ maps. For areas where anthropogenic activities with spatially differentiated treatment are already present, the exclusive use of VIs for the delineation of MZs must be carried out with reservations. [ABSTRACT FROM AUTHOR]

Questions: Understanding spatial variation in floristic composition is crucial to quantify the extent, patchiness and connectivity of distinct habitats and their spatial relationships. Broad‐scale variation in floristic composition and the degree of uniqueness of different regions remains poorly mapped and understood in several areas across the globe. We here aim to map vegetation heterogeneity in Amazonia. Location: Middle Juruá river region, Amazonas State, Brazil. Methods: We mapped four plant groups by applying machine learning to scale up locally observed community composition and using environmental and remotely sensed variables as predictors, which were obtained as GIS layers. To quantify how reliable our predictions were, we made an assessment of model transferability and spatial applicability. We also compared our floristic composition map to the official Brazilian national‐level vegetation classification. Results: The overall performance of our floristic models was high for all four plant groups, especially ferns, and the predictions were found to be spatially congruent and highly transferable in space. For some areas, the models were assessed not to be applicable, as the field sampling did not cover the spectral or environmental characteristics of those regions. Our maps show extensive habitat heterogeneity across the region. When compared to the Brazilian vegetation classification, floristic composition was relatively homogeneous within dense forests, while floristic heterogeneity in rainforests classified as open was high. Conclusion: Our maps provide geoecological characterization of the regions and can be used to test biogeographical hypotheses, develop species distribution models and, ultimately, aid science‐based conservation and land‐use planning. [ABSTRACT FROM AUTHOR]

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]