TREES OUTSIDE FORESTS: TECHNOLOGICAL ADVANCEMENT FOR SUSTAINABLE LAND USE

Abstract

TOF trees represent a vital yet underrepresented component of global and national tree cover, found on agricultural lands, residential areas, urban areas, roadside and linear landscape. In the past, finescale distribution and the small sizes of the crowns and spectral similarity between the crops had underscored dispersed and linear TOF by the field surveys and the medium spectral resolution satellites. TOF mapping has been under recent technological advances: high-resolution optical satellites (World View, Planet Scope, Sentinel-2), hyperspectral sensors (PRISMA, EnMAP, DESIS), UAV-based photogrammetry, LiDAR and Synthetic Aperture Radar have been used to make precise detections, delineate crowns, identify species in a qualitatively and quantitatively manner and estimate biomass. Machine learning and deep learning, including object-based image analysis, enable robust multi-temporal monitoring and mapping of individual trees and linear plantations. Enhanced multi-sensor integration, realtime UAV monitoring, and standardised governance mechanisms improve spatial resolution, analytical consistency, and the systematic embedding of TOF within climate-smart agroforestry and sustainable landuse frameworks.