Understanding of people’s landscape preferences is important for decision-making about land planning, particularly in the disturbance patterns that usually occur in rural-urban gradients. However, the use of different types of images concerning the same landscape may influence social preferences and thus perceptions of landscape management and planning decisions. We evaluated landscape preferences and perceptions in four landscapes of southern Chile. We specifically: (1) compared people’s perceptions related to living in, visiting, the scenic beauty, well-being, risks, and level of landscape disturbance; and (2) evaluated the influence of the type of scene (i.e., eye-level or aerial images) in these preferences and perceptions. Preferences and perceptions resulted to be better when using eye-level (4.0 ± 1.1) than aerial (3.7 ± 0.6) images. In general, we observed a negative association between preferences and perceptions and the landscape disturbance; however, it was consistent when using aerial images but was masked when valuing landscape through eye-level images. Implications of these results are relevant because by far, most landscape preference studies use traditional eye-level images. Different types of scenes should be considered in order to embrace the landscape preferences and perceptions of all those involved and help decision-making in landscape planning.

Data collection from large areas of native forests poses a challenge. The present study aims at assessing the use of UAV for forest inventory on native forests in Southern Chile, and seeks to retrieve both stand and tree level attributes from forest canopy data. Data were collected from 14 plots (45 × 45 m) established at four locations representing unmanaged Chilean temperate forests: seven plots on secondary forests and seven plots on old-growth forests, including a total of 17 different native species. The imagery was captured using a fixed-wing airframe equipped with a regular RGB camera. We used the structure from motion and digital aerial photogrammetry techniques for data processing and combined machine learning methods based on boosted regression trees and mixed models. In total, 2136 trees were measured on the ground, from which 858 trees were visualized from the UAV imagery of the canopy, ranging from 26% to 88% of the measured trees in the field (mean = 45.7%, SD = 17.3), which represented between 70.6% and 96% of the total basal area of the plots (mean = 80.28%, SD = 7.7). Individual-tree diameter models based on remote sensing data were constructed with R² = 0.85 and R² = 0.66 based on BRT and mixed models, respectively. We found a strong relationship between canopy and ground data; however, we suggest that the best alternative was combining the use of both field-based and remotely sensed methods to achieve high accuracy estimations, particularly in complex structure forests (e.g., old-growth forests). Field inventories and UAV surveys provide accurate information at local scales and allow validation of large-scale applications of satellite imagery. Finally, in the future, increasing the accuracy of aerial surveys and monitoring is necessary to advance the development of local and regional allometric crown and DBH equations at the species level.

Urban vegetation plays a vital role in mitigating the impacts of urbanization, improving biodiversity, and providing key ecosystem services. However, the spatial distribution, ecological dynamics, and social implications of urban vegetation remain insufficiently understood, particularly in underrepresented regions. This systematic review aims to synthesize global research trends in very-high-resolution (VHR) remote sensing of urban vegetation between 2000 and 2024. A total of 123 peer-reviewed empirical studies were analyzed using bibliometric and thematic approaches, focusing on the spatial resolution (<10 m), sensor type, research objectives, and geographic distribution. The findings reveal a predominance of biophysical studies (72%) over social-focused studies (28%), with major thematic clusters related to urban climate, vegetation structure, and technological applications such as UAVs and machine learning. The research is heavily concentrated in the Global North, particularly China and the United States, while regions like Latin America and Africa remain underrepresented. This review identifies three critical gaps: (1) limited research in the Global South, (2) insufficient integration of ecological and social dimensions, and (3) underuse of advanced technologies such as hyperspectral imaging and AI-driven analysis. Addressing these gaps is essential for promoting equitable, technology-informed urban planning. This review provides a comprehensive overview of the state of the field and offers directions for future interdisciplinary research in urban remote sensing.