## 1 Introduction  
Forest ecosystems have played a major role in human history, and forest management has accompanied population growth and development worldwide for thousands of years [1]. The extent of anthropogenic alteration and degradation of natural forests in many tropical and subtropical ecosystems is one of the main causes of biodiversityloss worldwide [2; 3]. Forest degradation is a process that diminishes the biodiversity and functioning of these ecosystems [4]. Rather than reducing forest area, degradation involves the deterioration of the structure and composition of woody plant biomass, leading to a decline in the forest's overall quality [4]. Thus, degradation directly results in a long-term loss of the forest's capacity for carbon storage [5] and its ability to provide goods and services [6]. Identifying effective strategies for forest management is a crucial challenge of the Anthropocene Epoch. In response to this challenge, the United Nations included forest management and governance strategies in the 2030 Sustainable Development Agenda (SDG) [7]. The 15 th SDG states, \"Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss\". Achieving this goal requires significant efforts to develop effective monitoring tools that illustrate sustainable management practices and support policymakers [1]. In this context, remote sensing (RS) could provide valuable support as an effective source of data and methods for forest monitoring, offering a cost-effective option for frequent observation of large areas [8]. RS provides multi-temporal data at various spatial scales in a cost-effective, spatially contiguous, and timely manner, supporting standardized assessments of forest cover and fragmentation [1; 9]. However, while RS is particularly effective for producing forest cover maps with explicit information on forest distribution, using RS to quantify forest degradation is more challenging. In such cases, the area remains forested but undergoes structural, compositional, and functional alterations [10]. Recent advancements in satellite imaging, algorithms, and computing now enable high-resolution, large-scale mapping of forest disturbances [8]. While most studies focus on boreal, temperate, and tropical wet forests, the mapping of forest alterations in subtropical semi-arid regions has only recently gained attention [11; 12]. To accurately assess forest characteristics in bio-climatic regions with strong seasonality, RS techniques can be combined with approaches that capture spatial and temporal vegetation patterns [13]. For instance, analyzing ecosystem phenological properties, which are driven by climatic seasonality [14], is an effective method for mapping intense seasonal biomass variations, such as those that characterize subtropical forests [15; 16]. The implementation of RS for mapping forest characteristics worldwide, particularly in subtropical and developing countries where natural forests face significant pressure, greatly benefits from the availability of free satellite imagery with good spatial resolution and high revisit frequency, such as that provided by the SENTINEL satellite constellation [8; 16]. These studies primarily rely on remote sensing data, as further research is needed to integrate remote sensing with conservation and alteration metrics obtained through field measurements [17; 18]. The Gran Chaco, one of the largest seasonally dry subtropical forests in the world, spans Argentina, Paraguay, Brazil, and Bolivia [19; 20]. It is also one of the most threatened ecosystems in Latin America [3]. Forest fragmentation and degradation, driven by unsustainable practices such as logging, firewood collection, charcoal production, and livestock grazing [9; 21; 22; 23; 24], have significantly impacted the Chaco Forest.