Thinning- and tree-growth-caused changes in canopy cover and stand height and their estimation using low-density bitemporal airborne lidar measurements – a case study in hemi-boreal forests

Abstract

Repeated airborne laser scanning (ALS) measurements during leaf-on and leaf-off phenophases were studied. A 15 km × 15 km test site located in northern Estonia was used that included a reference set of stands, and 870 stands with thinning carried out before, between, and after two ALS flights. The decrease in ALS-based canopy cover estimate (CC ALS ) caused by thinning was similar for the leaf-off and leaf-on phenophases, and for different height thresholds. The point cloud height percentile (H Px ) values increased in almost all thinned stands, and the increase was present for the leaf-off and leaf-on phenophases. ALS point cloud metrics (skewness, kurtosis, mode, and canopy relief ratio) showed no response to thinning (p-value >0.05). Stand-dominating species had no significant influence on H Px increment or CC ALS change using the leaf-on data (p-value >0.05). The minimum height filter for pulse return selection had a substantial influence on H Px increment in stands thinned between the two ALS measurements. Ground points are usually excluded from H Px calculation, but for stand-level analyses, their inclusion can provide additional information. ARTICLE HISTORY

Remote-sensing support for the Estonian National Forest Inventory, facilitating the construction of maps for forest height, standing-wood volume, and tree species composition

“…When the system becomes fully operational, it will be possible to determine also forest age at high accuracy, by using time series of multi-temporal satellite images for the detection of forest regeneration fell-ings (Peterson et al, 2004;Liira et al, 2006). Weak disturbances, such as normal thinnings, can also be detected from bi-temporal multispectral satellite image pairs (Uiga et al, 2003), as well as from sparse point clouds obtained through repeated ALS measurements (Arumäe, et al, 2020). With the combination of these various refinements in technique, Estonian forestry is entering an era in which data for forests at every point of the country are updated yearly and can be used for sustainable forest-management planning, on the basis of 10-30 m spatial units.…”

Aerolaserskaneerimise kasutamine metsakorralduse alusena

“…Maa-ameti tehtud lidarmõõtmiste andmebaasis on kogu Eesti kohta juba vähe malt kahekordne andmekiht, mis annab võimaluse lisaks eespool mainitud tunnuste hetkeväärtustele tuvastada ja hinnata erinevate tunnuste muutuseid (joonis 2). Peamised huvipakkuvad muutused on metsade juurdekasv ja kõrguse kasv (Arumäe et al, 2020). Aegridade olemasolu annab võimalusi puistute boniteerimiseks sobilike mudelite loomisel (Noordermeer et al, 2020;Guerra-Hernández et al, 2021), ning samuti harvendusraiete ja nendega sarnanevate häiringute tuvastamiseks, kui muutub puistu domineeriva rinde võrastiku tihedus või kõrgus Noordermeer et al, 2019b;Arumäe et al, 2020).…”

“…Another indicator variable for competition and stand density is ALS-based canopy cover (model 1) Arumäe, 2020), which is also the main variable in standing wood volume models. With multitemporal data available, different changes in forest structure can be monitored, like height growth (Figure 2) or small-scale harvests (Arumäe et al, 2020). Multitemporal data provides new options, but also raises the need to solve the variability in results, which is mostly related to differences between scanner and flight settings, and the influence of phenology (Figure 3).…”

An Evaluation of the Effects of UAS Flight Parameters on Digital Aerial Photogrammetry Processing and Dense-Cloud Production Quality in a Scots Pine Forest

“…Remote sensing (RS) includes the use of both active and passive sensing technologies for the measurement of surface characteristics from a distance [1,2]. Given its variable spatial scale, systematic acquisition schedule of some platforms, and diversity of sensors, RS data have been applied in various topics, such as land cover processes and atmospheric, hydrologic, oceanographic, and especially forest studies [1, [3][4][5]. In this context, RS applications for forests are complementary to traditional frameworks for data collection, minimizing spatial and cost limitations of the latter approach, and offer opportunities for accurate landscape-scale estimation of forestry inventory variables [6,7].…”