Combining ALS and UAV to derive the height of Araucaria angustifolia in the Brazilian Atlantic Rain Forest

Neto, Ernandes Macedo da Cunha; Veras, Hudson Franklin Pessoa; Moura, Marks Melo; Berti, André Luís; Sanquetta, Carlos Roberto; Pelissari, Allan Libanio; Corte, Ana Paula Dalla

doi:10.1590/0001-3765202320201503

An. Acad. Bras. Ciênc.

2023

DOI: 10.1590/0001-3765202320201503

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Combining ALS and UAV to derive the height of Araucaria angustifolia in the Brazilian Atlantic Rain Forest

Ernandes Macedo da Cunha Neto

Hudson Franklin Pessoa Veras

Marks Melo Moura

et al.

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2024

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Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

Pavani-Biju,

Borges,

Marques

et al. 2024

Sustainability

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Forest managers need inventory data and information to address sustainability concerns over extended temporal horizons. In situ information is usually derived from field data and computed using appropriate equations. Nonetheless, fieldwork is time-consuming and costly. Thus, new technologies like Light Detection and Ranging (LiDAR) have emerged as an alternative method for forest assessment. In this study, we evaluated the accuracy of geostatistical methods in predicting the Site Index (SI) using LiDAR metrics as auxiliary variables. Since primary variables, which were obtained from forestry inventory data, were used to calculate the SI, secondary variables obtained from LiDAR surveying were considered and multivariate kriging techniques were tested. The ordinary cokriging (CK) method outperformed the simple cokriging (SK) and Inverse Distance Weighted (IDW) methods, which was interpolated using only the primary variable. Aside from having fewer SI sample points, CK was proven to be a trustworthy interpolation method, minimizing interpolation errors due to the highly correlated auxiliary variables, highlighting the significance of the data's spatial structure and autocorrelation in predicting forest stand attributes, such as the SI. CK increased the SI prediction accuracy by 36.6% for eucalyptus, 62% for maritime pine, 72% for pedunculate oak, and 43% for cork oak compared to IDW, outperforming this interpolation approach. Although cokriging modeling is challenging, it is an appealing alternative to non-spatial statistics for improving forest management sustainability since the results are unbiased and trustworthy, making the effort worthwhile when dense secondary variables are available.

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Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

Pavani-Biju,

Borges,

Marques

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

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Combining ALS and UAV to derive the height of Araucaria angustifolia in the Brazilian Atlantic Rain Forest

Cited by 1 publication

References 37 publications

Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

Enhancing Forest Site Classification in Northwest Portugal: A Geostatistical Approach Employing Cokriging

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