## 1 Introduction
Its usefulness is reflected in the different applications of these images for the detection of invasive species [26], the identification and classification of tree species [27], or the use of multitemporal data for the characterization of regenerating forest stands [28]. To enhance the discrimination of forest species, various studies have demonstrated the effectiveness of combining S1 and S2 data [21; 24], as well as their integration with topographic information [29], in Cloud Computing platforms such as Google Earth Engine (GEE) [30; 31; 32]. On the other hand, there is currently spectral information with better spatial resolution available through the PlanetScope platform, acquiring images with 3 m/pixel spatial resolution and capturing data in the red (R), green (G), blue (B), and near-infrared (NIR) channels. However, the open-access data provided by Norway's International Climate and Forests Initiative (NICFI) program, in collaboration with Planet Labs of the PlanetScope data, are available in semiannual or monthly mosaics with a resolution of 4.77 m per pixel. Some studies, such as the one from [PERSON] (2022), use these data in the GEE environment to compare ten land use and land cover (LULC) classification approaches, highlighting the advantages of adopting an object-based (OB) approach with PlanetScope (PL) data and integrating PL with Sentinel-2 and Sentinel-1 data. These studies incorporate Gray-Level Co-occurrence Matrix texture variables to aid classification [33]. Although remote sensing is widely applied in identifying, monitoring, and characterizing forest species, its implementation presents important challenges in the study of the genus _Polylepis_. Some studies are focused on this species, such as the one conducted by [PERSON] et al., who evaluated the distribution and cover of a _Polylepis_ species (_P. tarapacana_) and its relationship with topographic, environmental, and climatic factors in the Andean zone of northern Argentina, identifying that elevation and slope are significant factors in the distribution of this species within the study sector [11]. [PERSON] et al. [12] evaluated the possible distribution of _Polylepis quadrijuga_ in the Colombian Andes due to its ecological importance, using different future temporal scenarios, demonstrating that climate change could generate a considerable loss of suitable habitat for this species. [PERSON] et al. evaluated the unique climatic habitat conditions of _Polylepis Tarpacana_ in the Chilean Andes. They highlighted the urgent need to develop studies that analyze adaptive strategies for this species to withstand climate changes at high altitudes [34]. It is worth noting that studies employing pixel and object classification methods [35; 36] within the mountainous forests of the Andean region have major limitations [22], mainly due to the topographic complexity, shadows, and illuminated areas [35], the limited availability of cloud-free satellite images [8; 37], and the challenge of finding completely homogeneous pixels [22]. However, there are approximations through the detection of native forests with the combination of optical and radar features [38]. Combining remote sensing data with image processing and classification methods is useful to analyze and identify forest entities [39]. Several works present the feasibility of the evaluation of forest ecosystems through Machine Learning (ML) algorithms such as Random Forest (RF) [22; 30; 35; 36; 38; 40; 41; 42], Support Vector Machines [22; 35; 36; 43], Gradient Boosting [44], Naive Bayes [45], being RF one of the most used, especially in the discrimination and identification of mountain forests and shrublands [22]. Additionally, some researchers used hyperspectral images obtained from Unmanned Aerial Vehicles (UAVs) for the identification of _Polylepis_[10] and other forest species [46; 47; 48]. Despite this progress, several challenges remain in identifying _Polylepis_ using remote sensing.