High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry

Abstract

Traditional forest monitoring has been mainly performed with images or orthoimages from aircraft or satellites. In recent years, the availability of high-resolution 3D data has made it possible to obtain accurate information on canopy size, which has made the topic of canopy 3D growth monitoring timely. In this paper, forest growth pattern was studied based on a canopy point cloud (PC) reconstructed from UAV aerial photogrammetry at a daily interval for a year. Growth curves were acquired based on the canopy 3D area (3DA) calculated from a triangulated 3D mesh. Methods for canopy coverage area (CA), forest coverage rate, and leaf area index (LAI) were proposed and tested. Three spectral vegetation indices, excess green index (ExG), a combination of green indices (COM), and an excess red union excess green index (ExGUExR) were used for the segmentation of trees. The results showed that (1) vegetation areas extracted by ExGUExR were more complete than those extracted by the other two indices; (2) logistic fitting of 3DA and CA yielded S-shaped growth curves, all with correlation R2 > 0.92; (3) 3DA curves represented the growth pattern more accurately than CA curves. Measurement errors and applicability are discussed. In summary, the UAV aerial photogrammetry method was successfully used for daily monitoring and annual growth trend description.

GEOBIA and Vegetation Indices in Extracting Olive Tree Canopies Based on Very High-Resolution UAV Multispectral Imagery

“…Geospatial technologies and their products have become easily available and widely accessible in everyday life, which has advanced and accelerated their use in various research [16][17][18][19]. The development of advanced geospatial technologies has enabled PA in the olive management sector, especially in the context of precise canopy extraction for management [20][21][22], monitoring [23][24][25], assessment of crop quality [26], disease detection [27], and preventive actions [28]. Therefore, geospatial technologies have a meaningful role in the observation and analysis of various processes located on the Earth's surface.…”

Study on the Spatiotemporal Evolution and Influencing Factors of Forest Coverage Rate (FCR): A Case Study on Yunnan Province Based on Remote Sensing Image Interpretation

“…With the growth and maturity of "3S" technology (i.e., remote sensing (RS) technology, geographic information systems (GISs), and global positioning systems (GPSs)), significant progress has been made in using "3S" technology to study FCR, and this has gradually become a hot topic in the research; it is evident that increasingly more abundant research results are emerging in the literature (Li Yang et al, 2018;Zhang Yangjian et al, 2022;Yin Huiyan et al, 2020) [17][18][19]. Overall, the existing research conducted on forest coverage mainly includes the following categories: The first category involves studying the spatiotemporal changes in FCR from the perspectives of land use/land cover change (LUCC) ( [20][21][22][23]. The advantage of this type of research is the use of modern technologies, such as RS image interpretation, to accurately monitor the dynamic changes in FCR or relative indicators, and explore their evolution feature.…”

Exploring Airborne LiDAR and Aerial Photographs Using Machine Learning for Land Cover Classification

“…It can also obtain the shape, position, and characteristics of various objects in the three-dimensional space, which meets visual needs. The scope of application includes land cover classification [8], forest monitoring [9,10], plant growth analysis [11], and land disaster assessment [12].…”