High-resolution, dynamic geospatial data support sustainable infrastructure, optimize urban services, and improve the quality of life of residents. Airborne Laser Scanning is an increasingly popular remote sensing technology that can be used to collect very large datasets of 3D point clouds over extensive areas, including forests, river basins, coastal wetlands, and mountainous regions. These datasets facilitate the analysis of vegetation structure, biomass estimation, hydrological modeling, and land cover detection or change monitoring. However, Airborne Laser Scanning-derived point clouds are typically limited to low density and spatial resolution, which preclude meaningful analysis for fine-scale ecological and environmental modeling. Point cloud upsampling is a permissible way to augment the spatial robustness of Airborne Laser Scanning point cloud data, and does so without adding a logistical burden of the data acquisition in the field, or the need to resurvey at high costs and time. Upsampling is synthetic in nature, achieving increased data point count, but maintaining dimensional integrity for continuity of surfaces and geometric fidelity, which is essential in methodologies that intervene for derived products such as digital terrain models, canopy height models, and vegetation metrics. This manuscript examines using point cloud upsampling as part of environmental monitoring. It reviews the upsampling algorithms that have been developed to date, synthesizes existing methods, and considers their relevance to the state of practice in forestry, watershed management, and conservation planning. The work considers and focuses on methodological bases for robustness and dimensionality, and although considerably nuanced, the methodological efficacy is subtended and suggests how enhanced points improve outcomes for ecological models and the information provided supports resource management decisions related to resource and sustainability decisions.. Ultimately and conclusively, the work establishes the understanding of point cloud enhancement for its visualization, but also its potential as an emergent action that contributes to construction and promotes a sustainability intention in environmental science and policy. This article appears as a mini-review. This writing aims to synthesise existing knowledge and conceptual strategies, rather than a novel outcome of an experiment. It is written to give an overview of existing methods and structured conceptual frameworks for employing point cloud upsampling techniques in the environmental monitoring and sustainability context. The review reiterates the conceptual soundness of point cloud upsampling in the workflow of environmental monitoring. The proposed framework reinforces the benefits of greater data richness and decision-making based on sustainability, without assuming new costs for data generation.