Estimation of leaf area index and canopy openness in broad-leaved forest using an airborne laser scanner in comparison with high-resolution near-infrared digital photography

Abstract

We estimated leaf area index (LAI) and canopy openness of broad-leaved forest using discrete return and small-footprint airborne laser scanner (ALS) data. We tested four ALS variables, including two newly proposed ones, using three echo types (first, last, and only) and three classes (ground, vegetation, and upper vegetation), and compared the accuracy by means of correlation and regression analysis with seven conventional vegetation indices derived from simultaneously acquired high-resolution near-infrared digital photographs. Among the ALS variables, the ratio of the ''only-and-ground'' pulse to ''only'' pulse (OGF) was the best estimator of both LAI (adjusted R 2 = 0.797) and canopy openness (adjusted R 2 = 0.832), followed by the ratio of the pulses that reached the ground to projected lasers (GF). Among the vegetation indices, the normalized differential vegetation index (NDVI) was the best estimator of both LAI (adjusted R 2 = 0.791) and canopy openness (adjusted R 2 = 0.764). Resampling analysis on ALS data to examine whether the estimation of LAI and canopy openness was possible with lower point densities revealed that GF maintained a high adjusted R 2 until a fairly low density of about 0.226 points/ m 2 , while OGF performed marginally when the point density was reduced to about 1 point/m 2 , the standard density of high-density products on the market as of February 2008. Consequently, the ALS variables proposed in the present study, GF and OGF, seemed to have great potential to estimate LAI and canopy openness of broadleaved forest, with accuracy comparable to NDVI, from high-resolution near-infrared imagery.

Combined Use of Airborne Lidar and DBInSAR Data to Estimate LAI in Temperate Mixed Forests

“…In the past, models for LAI prediction in mixed hardwood and coniferous forests using only lidar data have reported R 2 values ranging from 0.8 to 0.9, using either very few plots (between 10 to 18) or small plot sizes (400 m 2 to 500 m 2 ) [8,9,12,38]. The results reported in this research, using 61 plots of 1,257 m 2 size, reveal an R 2 of 0.69 (CV-RMSE = 0.48) for lidar only models, and an increased R 2 value of 0.77 (CV-RMSE = 0.42) when using lidar and GeoSAR data together.…”

“…Later, Kwak et al [8] using the LPI and an interception index (LII) could explain 86% of the variation in a South Korean mixed forest. In Japan, using the ground fraction of returns, Sasaki et al [9] reported an adjusted R 2 of 0.80 for an evergreen and broad-leaved forest. Solberg et al [10] reported correlations with laser penetration from 0.37 to 0.93 (depending on plot size), in a mixed forest in Norway.…”

Influência da idade na contagem de árvores de Eucalyptus sp. com dados de lidar

“…ha -1 . Várias linhas de pesquisa para estimar parâmetros de parcelas, contagem de árvores individuais, quantificação do estoque florestal, biomassa e para a diminuição da intensidade amostral vêm sendo desenvolvidas, podendo-se destacar trabalhos de Crow et al (2007), Heurich e Thoma (2008), Ioki et al (2010), Jupp et al (2009), Lauri et al (2008, Maltamo et al (2004), Naesset e Bjerknes (2001), Popescu et al (2002), Sasaki et al (2008), Tiede et al (2005) e Yu et al (2004). No Brasil, essa linha de pesquisa é bem recente, em razão do custo para a aquisição dessas informações e também por apenas algumas empresas manusearem esse equipamento em aeronaves.…”

Estimation of aerial biomass using discrete-wave LiDAR data in combination with different vegetation indices in plantations of Pinus radiata (D. DON), Región del Maule, Chile.

“…He also used a sub-stratification using the We also explored transformations of LiDAR data, from which a new variable of point density was constructed which has not been previously reported. The construction of this type of information was inspired by the studies of Ioki et al (2009) y Sasaki et al (2008; both studies used transformations which combine relations between ground points and aerial points, assuming some type of relational proportion to the density of the vegetation. The entrance data for this study did not include information on the return number, which is necessary to use the models of these two groups;…”