Species dominance and above ground biomass in the Bialowieza Forest, Poland, described by airborne hyperspectral and lidar data

编号 030025801

推送时间 20200928

研究领域 森林经理 

年份 2020 

类型 期刊 

语种 英语

标题 Species dominance and above ground biomass in the Bialowieza Forest, Poland, described by airborne hyperspectral and lidar data

来源期刊 INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION

期 第258期

发表时间 20200620

关键词 REMOTE-SENSING DATA;  VEGETATION INDEXES;  TEMPERATE FOREST;  CLIMATE-CHANGE;  ALS DATA;  CLASSIFICATION;  VOLUME;  DELINEATION;  PREDICTION;  IMPACTS; 

摘要 The objective of this research is to test and evaluate hyperspectral and lidar data to derive information on tree species dominance and above ground biomass in the Bialowieza Forest in Poland. This forest is threatened by climate change, fire, bark beetles attacks, and logging, with changes in species composition and dominance. In this conservation valuable area, the monitoring of forest resources is thus critical.
Results indicate that vegetation indices from hyperspectral data can support species dominance detection: using a Classification and Regression Trees algorithm the three main plot types (dominated by Deciduous, Spruce, and Pines species) were classified with an Overall Accuracy > 0.9. The accuracy decreased when a 'Mixed' group was added to account for very heterogeneous plots, and plots dominated by Spruce were not correctly detected. Hyperspectral vegetation indices were also used to estimate the level of species dominance in the forest plots, using a Multivariate Multiple Linear Regression model; the obtained accuracy varied according to groups, being higher for Deciduous (R-2 = 0.87), compared to Pines (R-2 = 0.61), and to Spruce-dominated plots (R-2 = 0.37).
Lidar data were employed to estimate above ground biomass, using an exponential regression model; overall the R-2 resulted equal to 0.66 but ranged from 0.57 to 0.78 when considering subgroups according to species dominance; the addition of hyperspectral vegetation indices improved the result only for Pines.
The illustrated methods provide a reliable description of important forest characteristics and simplify resource monitoring, supporting local authorities to address the challenges imposed by climate change and other forest threats.

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