Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations

Görgens, Eric Bastos; Packalén, Petteri; Silva, André Gracioso Peres da; Álvares, Clayton Alcarde; Campoe, Otávio Camargo; Stape, José Luiz; Rodriguez, Luiz Carlos Estraviz

doi:10.1007/s13595-015-0457-x

Cited by 24 publications

(23 citation statements)

References 28 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Conversely, the classification tree model developed in our study, among the original eighteen variables, it selected five variables: standard deviation and mode of canopy heights, height at which 95% and 99% of canopy heights fall below, difference between height at which 90% and 10% of canopy heights fall below. This confirm Görgens et al (2015b) findings about the specificity of LiDAR metrics as predictors and the need of identifying stable metrics derived from ALS data to be used as independent variable in specific models. Classification trees are useful tools with a simple structure, that is with low number of rules and final nodes.…”

Section: Discussionsupporting

confidence: 85%

Using classification trees to predict forest structure types from LiDAR data

Torresan

Corona

Scrinzi³

et al. 2016

Ann. For. Res.

View full text Add to dashboard Cite

Torresan C., Corona P., Scrinzi G., Marsal J.V., 2016. Using classification trees to predict forest structure types from LiDAR data. Ann. For. Res. 59(1): 281-298.Abstract. This study assesses whether metrics extracted from airborne Li-DAR (Light Detection and Ranging) raw point cloud can be exploited to predict different forest structure types by means of classification trees. Preliminarily, a bivariate analysis by means of Pearson statistical test was developed to find associations between LiDAR metrics and the proportion of basal area into three stem diameter classes (understory, mid-story, and over-story trees) of 243 random distributed plots surveyed from 2007 to 2012 in Trento Province (Northern Italy). An unsupervised clustering approach was adopted to determine forest structural patterns on the basis of basal area proportion in the three stem diameter classes, using a k-means procedure combined with a previous hierarchical classification algorithm. A comparison among the identified clusters centroids was performed by the Kruskall-Wallis test. A classification tree model to predict forest structural patterns originating from the cluster analysis was developed and validated. Between 18 potential LiDAR metrics, 11 were significantly correlated with the proportion of basal area of understory, mid-story, and overstory trees. The results coming from the agglomerative hierarchical clustering allowed identification of 5 clusters of forest structure: pole-stage (70% of the considered cases), young (15%), adult (24.3%), mature (24.3%), and old forests (30%). Five LiDAR metrics were selected by the classification tree to predict the forest structural types: standard deviation and mode of canopy heights, height at which 95% and 99% of canopy heights fall below, difference between height at which 90% and 10% of canopy heights fall below. The validation tree model process showed a misclassification error of 45.9% and a level of user's accuracy ranging between 100% and 33.3% in the validation data set. The highest level of user's accuracy was reached in the classification of pole-stage forests (100%), in which more than 82% of basal area is due to the understory-trees, follow by the classification of old forests types (63.5% of basal area due to the overstory-trees) achieved 76.5% of user's accuracy. The model has provided moderately satisfactory results in term of classification performance: substantial room for improvement might be established by multi-or hyperspectral imaging that allow detailed characterization of the spectral behaviour of the forest structure types.

show abstract

Section: Discussionsupporting

confidence: 85%

Using classification trees to predict forest structure types from LiDAR data

Torresan

Corona

Scrinzi³

et al. 2016

Ann. For. Res.

View full text Add to dashboard Cite

show abstract

“…This is because the most productive plots also had more ALS points intercepted by the crown, thereby underestimating the density of trees in the understory layer. Additionally, observations were influenced by the point density heterogeneity within the ALS data, partially created by the overlapping of swaths during the flight (Bater et al, 2011;Görgens et al, 2015a). The selected regression models had at most two explanatory variables, and they were able to explain 93 % of the stem biomass variation.…”

Section: Discussionmentioning

confidence: 99%

Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations

Silva

Görgens

Campoe

et al. 2015

Sci. agric. (Piracicaba, Braz.)

Self Cite

View full text Add to dashboard Cite

This study aimed to map the stem biomass of an even-aged eucalyptus plantation in southeastern Brazil based on canopy height profile (CHPs) statistics using wall-to-wall discrete return airborne laser scanning (ALS), and compare the results with alternative maps generated by ordinary kriging interpolation from field-derived measurements. The assessment of stem biomass with ALS data was carried out using regression analysis methods. Initially, CHPs were determined to express the distribution of laser point heights in the ALS cloud for each sample plot. The probability density function (pdf) used was the Weibull distribution, with two parameters that in a secondary task, were used as explanatory variables to model stem biomass. ALS metrics such as height percentiles, dispersion of heights, and proportion of points were also investigated. A simple linear regression model of stem biomass as a function of the Weibull scale parameter showed high correlation (adj.R² = 0.89). The alternative model considering the 30th percentile and the Weibull shape parameter slightly improved the quality of the estimation (adj. R² = 0.93). Stem biomass maps based on the Weibull scale parameter doubled the accuracy of the ordinary kriging approach (relative root mean square error = 6 % and 13 %, respectively).

show abstract

“…Entretanto técnicas passivas de sensoriamento remoto possuem limitações em sua capacidade de diferenciar mudanças na estrutura que ocorrem abaixo do dossel (COOPS et al, 2007), fornecendo apenas informações detalhadas sobre a distribuição horizontal (LIM et al, 2003). EGGERMONT; LaRICCIA, 1999;COOPS et al, 2007;GÖRGENS et al, 2015;SILVA et al, 2015). Coops et al (2007) Se não houver flores, valeu a sombra das folhas;…”

Section: Caracterização Da Estrutura Da Florestaunclassified

“…A natureza tridimensional dos dados ALS permite a caracterização da distribuição da vegetação, tanto horizontal como vertical (LIM et al, 2003), além de fornecer estimativas precisas sobre as alturas e cobertura do dossel (COOPS et al, 2007). As métricas derivadas de dados ALS vem sendo utilizadas nas estimativas de volume, biomassa, área basal e diâmetro médio quadrático (WHITE et al, 2013;GÖRGENS et al, 2015;PALACE et al, 2015;SILVA et al, 2015). Além desses aspectos, uma série de métricas que descrevem a variabilidade da distribuição dos retornos são adequadas para caracterizar a estrutura do dossel (COOPS et al, 2007;KANE et al, 2010 (FIEBER et al, 2015).…”

Section: Introductionunclassified

“…Esses estudos têm-se concentrado em analisar a precisão dos dados ALS para estimar atributos dos talhões (WACK et al, 2003;JUPP et al, 2005;TESFAMICHAEL et al, 2010aTESFAMICHAEL et al, , 2010bGONÇALVES-SECO et al, 2011;PACKALÉN et al, 2011aPACKALÉN et al, , 2011bVAUHKONEN et al, 2011;GÖRGENS et al, 2015). Zonete et al (2010) demonstraram o elevado potencial do uso de métricas ALS como preditoras de parâmetros de inventário florestal, tais como diâmetro médio, altura média, altura grandis e E. urophylla (E), conhecido como E. urograndis, foi desenvolvido para aumentar a produtividade, a tolerância a pragas e doenças, com excelente enraizamento, e é uma das principais espécies atualmente utilizadas para a produção de celulose no Brasil (SILVERIO et al, 2007;REZENDE et al, 2014).…”

Section: Introductionunclassified

See 1 more Smart Citation

Estudo de parâmetros populacionais de plantios clonais de <i>Eucalyptus</i> spp. a partir de nuvens de pontos obtidos com escaneamento a laser aerotransportado

Laranja¹

View full text Add to dashboard Cite

Estudo de parâmetros populacionais de plantios clonais deAs florestas plantadas contribuem para o desenvolvimento econômico, social e ambiental brasileiro, e o setor florestal busca plantios cada vez mais produtivos e melhor aproveitados. A tecnologia a laser aerotransportada (ALS -Airborne Laser Scanning), capaz de gerar informações tridimensionais precisas em extensas áreas e em pequeno intervalo de tempo, tem se destacado nas aplicações florestais, sendo utilizada na quantificação e caracterização de florestas. Neste estudo foram utilizados dados ALS obtidos em dois sobrevoos (2013 e 2014), cobrindo uma área de plantio do gênero Eucalyptus com diferentes clones e idades, localizada no estado de São Paulo. Esta dissertação é dividida em três partes. A primeira buscou avaliar a combinação de métricas ALS e de dupla amostragem nas estimativas de volume e na redução da intensidade amostral, comparando seus resultados aos de métodos amostrais tradicionais. Os resultados mostraram que a dupla amostragem obteve o menor erro de amostragem dentre os delineamentos, permitindo a redução do esforço amostral. A segunda parte visou caracterizar as diferenças estruturais existentes entre clones de eucalipto, com diferentes idades, a partir de métricas ALS e perfis de altura do dossel (CHP). O estudo mostrou que é possível distinguir a estrutura do dossel dos clones, o que pode contribuir com melhorias nas estimativas e no gerenciamento dos plantios comerciais. Na última parte, foram avaliados dois algoritmos de detecção de árvores individuais quanto à quantificação dos indivíduos e os efeitos da composição (tamanho de célula e filtragem) do modelo digital de alturas (CHM) no desempenho desses algoritmos. Verificou-se que as características do CHM influenciam na detecção. A estratégia com melhor desempenho resultou em um erro médio relativo de 11% no número de indivíduos. Planted forests contribute to the economic, social and environmental development of Brazil, and the forest industry aims to increase productivity and the efficient use of these plantations. Airborne Laser Scanning (ALS) technology, capable of generating accurate three-dimensional information, covering large areas in a short time, has highlighted potential in forestry applications and has been applied in forests quantification and characterization. In this study, ALS data were obtained from two flights (2013 and 2014), covering a Eucalyptus plantation area with different clones and ages, in the state of São Paulo. This thesis comprises three parts. The first study aimed to evaluate the combination of ALS metrics and double sampling design for volume estimates and reducing sampling intensity, comparing the results to traditional sampling methods. Results showed that the double sampling had the lowest error from the designs, allowing the reduction of the sampling effort. The second part was proposed to characterize the structural differences between Eucalyptus clones with different ages, based on metrics ALS and canopy height profiles (CHP). The study showed t...

show abstract

Stand volume models based on stable metrics as from multiple ALS acquisitions in Eucalyptus plantations

Cited by 24 publications

References 28 publications

Using classification trees to predict forest structure types from LiDAR data

Using classification trees to predict forest structure types from LiDAR data

Assessing biomass based on canopy height profiles using airborne laser scanning data in eucalypt plantations

Estudo de parâmetros populacionais de plantios clonais de <i>Eucalyptus</i> spp. a partir de nuvens de pontos obtidos com escaneamento a laser aerotransportado

Contact Info

Product

Resources

About