Continuous mapping of vegetation height is critical for many forestry applications, such as planning vegetation management in power transmission line right-of-way. Satellite images from different sensors, including SAR (Synthetic Aperture Radar) from Sentinel 1 (S1) and multispectral from Sentinel 2 (S2), can be used for producing high-resolution vegetation height maps at a broad scale. The main objective of this study is to assess the potential of S1 and S2 satellite data, both in a single and a multisensor approach, for modeling canopy height in a transmission line right-of-way located in the Atlantic Forest of Paraná, Brazil. For integrating S1 and S2 data, we used three machine learning algorithms (LR: Linear Regression, CART: Classification and Regression Trees, and RF: Random Forest) and airborne LiDAR (Light Detection and Ranging) measurements as the reference height. The best models were obtained using the RF algorithm and 20 m resolution features from only S2 data (cross-validated RMSE of 4.92 m and R² of 0.58) or multisensor data (cross-validated RMSE of 4.86 m and R² of 0.60). Although the multisensor model presented the best performance, it was not statistically different from the single-S2 model. Thus, the use of only S2 to estimate canopy height has practical advantages, as it reduces the need to process SAR images and the uncertainties due to S1 noise or differences between the acquisition dates of S2 and S1.

The installation of Transmission Lines (TL) provokes disturbances in the environment, which reacts in different ways. The suppression of vegetation, even when limited to what is necessary for the execution of the project, may cause erosive processes. Spatialized inputs have made the monitoring of large portions of land viable. Data from the GPM (Global Precipitation Measurement) constellation allows access to rainfall data even in areas which do not have pluviometric stations. Also by satellite, the landscape can be represented with SRTM (Shuttle Radar Topography Mission) images. Similarly, maps of soil erodibility and land use, necessary together so that the diagnostics methodology on the risk of erosion can be obtained. Therefore, a logic is methodologically developed in which freely obtained input data follow a conceptual model, which defines the variables and their relationships in the potential issue’s context, while having replication accessibility in mind. Of the 43 gaps present in the pilot tracing located in Minas Gerais, Brazil, four of them received the “likely” risk of erosion classification in a period without record of any meaningful rainfall. The methodology mirrors an evaluation which increases the risk as long as the rains occur.