A Camera-Based Method for Collecting Rapid Vegetation Data to Support Remote-Sensing Studies of Shrubland Biodiversity

Abstract

The decline in biodiversity in Mediterranean-type ecosystems (MTEs) and other shrublands underscores the importance of understanding the trends in species loss through consistent vegetation mapping over broad spatial and temporal ranges, which is increasingly accomplished with optical remote sensing (imaging spectroscopy). Airborne missions planned by the National Aeronautics and Space Administration (NASA) and other groups (e.g., US National Ecological Observatory Network, NEON) are essential for improving high-quality maps of vegetation and plant species. These surveys require robust and efficient ground calibration/validation data; however, barriers to ground-data collection exist, such as steep terrain, which is a common feature of Mediterranean-type ecosystems. We developed and tested a method for rapidly collecting ground-truth data for shrubland plant communities across steep topographic gradients in southern California. Our method utilizes semi-aerial photos taken with a high-resolution digital camera mounted on a telescoping pole to capture groundcover, and a point-intercept image-classification program (Photogrid) that allows efficient sub-sampling of field images to derive vegetation percent-cover estimates while reducing human bias. Here, we assessed the quality of data collection using the image-based method compared to a traditional point-intercept ground survey and performed time trials to compare the efficiency of various survey efforts. The results showed no significant difference in estimates of percent cover and Simpson’s diversity derived from the point-intercept and those derived using the image-based method; however, there was lower correspondence in estimates of species richness and evenness. The image-based method was overall more efficient than the point-intercept surveys, reducing the total survey time by 13 to 46 min per plot depending on sampling effort. The difference in survey time between the two methods became increasingly greater when the vegetation height was above 1 m. Due to the high correspondence between estimates of species percent cover derived from the image-based compared to the point-intercept method, we recommend this type of survey for the verification of remote-sensing datasets featuring percent cover of individual species or closely related plant groups, for use in classifying UAS imagery, and especially for use in MTEs that have steep, rugged terrain and other situations such as tall, dense-growing shrubs where traditional field methods are dangerous or burdensome.

Detecting and distinguishing between apicultural plants using UAV multispectral imaging

“…Being able to track such predators could help in the control of their spread and limit their impact on honeybees. The use of UAVs equipped with multispectral sensors is nowadays used as a tool for monitoring invasive plants ( Elkind et al, 2019 ) and wild plant species in inaccessible areas and with zero environmental disturbance ( Questad et al, 2022 ). Success in identifying Thyme plants, the dominant apicultural plant of Lemnos Island and most Greek Islands, is of great importance for beekeeping in Greece.…”

Monitoring Seedling Emergence, Growth, and Survival Using Repeat High-Resolution Imagery

“…The use of remote cameras for research has varied widely and includes applications such as the estimation of tiger densities in India [32], monitoring wildlife interactions with feral horses at water sources in the Great Basin [33], determining watering sites selected by chukar [34], and monitoring invasive rodents and rodent granivory [35,36]. Compared to wildlife monitoring, literature describing the use of remote cameras for monitoring vegetation is limited, however, this technology is currently being applied to improve vegetation detection, monitoring, and habitat assessment [30,37,38].…”