## 1 Introduction
The remote sensing-based assessment of forest study is repetitive, affordable, competent, and non-destructive for monitoring [25]. Recently, in complementarity with field data, it has shown great adaptability in environmental studies such as droughts [26], floods [27], the spread of invasive species [28], disturbance [29], and other human-induced pressures [30]. The contribution of satellite data is becoming impressive to monitor the spatial distribution and temporal dynamics of plantation forests [31]. Optical data are spectrally sensitive to different species and can distinguish phenological characteristics unique to a particular plantation [32]. Different plantation forests can have distinct implications for the local ecosystem service [33]. The geographic distribution of species is dynamic at accelerating rates because of anthropogenic pressures, the introduction of non-native species, and climate change [34]. To understand the distribution of introduced or expanding species, researchers often map the suitability status of the habitat or the potential occurrence probability of species using different techniques such as expert opinion [35], mathematical models [36], or machine learning algorithms [37]. These methods help researchers and decision-makers to identify priority areas for environmental conservation [38], examine landscape planning approaches on the management and restoration of protected areas [39], assess species distribution under changing anthropogenic or environmental conditions [40], investigate the impact of environmental changes on the biodiversity [41], and model the invasion status of invasive species [28; 42]. The species distribution model (SDM) is a popular technique in ecology and conservation biology to assess the impact of land use and climate changes on biodiversity distribution [43], predict species diversity and composition patterns over space and time [44], and provide spatially explicit and compressive maps that are specifically important to understand the distribution level and extent of a given species. It combines observations of species occurrence or abundance with environmental variables [45]; its performance depends on the collected data during field surveys and exists as simple presence/absence records, which are crucial to train and validate the model [46]. Moreover, the accuracy of SDM varies among algorithms [47], and integrating multiple algorithms is more reliable to get robust estimations of species distribution [48]. The use of SDM algorithms in combination with remotely sensed datasets is effective for mapping plant species across different management levels at local and regional scales [49]. In Sub-Saharan Africa, agricultural landscapes are highly fragmented and this is one of the challenges complicating their mapping [50]. Fragmented parcels of land with verities of coverage highlighting the need of methods based on high resolution satellite imageries [51]. PlanetScope (PS) satellite constellation can achieve daily coverage with a spatial resolution of 3-5 m, visible to near-infrared and atmospherically corrected imagery [52], which has been successfully applied in many fields, for example, rubber plantation mapping [53], forest canopy height estimation [54], biomass estimation [55], leaf area index production [56], cropland mapping [51], and crop yield prediction [57]. It provides effective spatial data for the extraction of plantation forest and agricultural information in the tropical and subtropical regions [58], and offers a good opportunity to overcome challenges in mapping smallholder agricultural fields [51]. Understanding the interplay between the spatial distribution of a species and its environmental determinants is a fundamental concept in ecology and conservation [59]. Establishing plantation forests on agricultural or degraded land presents significant prospects for biodiversity conservation [60]. Consequently, the impact of plantation forests is contingent upon their spatial extent of landscape coverage [61] and the specific land use they replace [4].