Abstract
Knowledge of tree species composition is obligatory in forest management. Accurate tree species maps allow for detailed analysis of a forest ecosystem and its interactions with the environment. The research presented here focused on developing methods of tree species identification using aerial hyperspectral data. The research area is located in Southwestern Poland and covers the Karkonoski National Park (KNP), which was significantly damaged by acid rain and pest infestation in the 1980s. High-resolution (3.35 m) Airborne Prism Experiment (APEX) hyperspectral images (288 spectral bands in the range of 413 to 2440 nm) were used as a basis for tree species classification. Beech (Fagus sylvatica), birch (Betula pendula), alder (Alnus incana), larch (Larix decidua), pine (Pinus sylvestris), and spruce (Picea abies) were classified. The classification algorithm used was feed-forward multilayered perceptron (MLP) with a single hidden layer. To simulate such a network, we used the R programming environment and the nnet package. To provide more accurate measurement of accuracy, iterative accuracy assessment was performed. The final tree species maps cover the whole area of KNP; a median overall accuracy (OA) of 87% was achieved, with median producer accuracy (PA) for all classes exceeding 68%. The best-classified classes were spruce, beech, and birch, with median producer accuracy of 93%, 88% and 83%, respectively. The pine class achieved the lowest median producer and user accuracies (68% and 75%, respectively). The results show great potential for the use of hyperspectral data as a tool for identifying tree species locations in diverse mountainous forest.
Highlights
Managing forest resources requires a detailed inventory of forest species and information about changes occurring in the forest ecosystem, in both biotic and abiotic components
The main focus of forest management and nature protection endeavors should be on the monitoring of species composition changes over time, spatial distribution of species, and vegetation conditions [1]
When comparing the present classification results with those obtained by other researchers using remote sensing data, the data and classification algorithm used must be taken into account, as well as the number of classes determined by individual authors
Summary
Managing forest resources requires a detailed inventory of forest species and information about changes occurring in the forest ecosystem, in both biotic and abiotic components. The main focus of forest management and nature protection endeavors should be on the monitoring of species composition changes over time, spatial distribution of species, and vegetation conditions [1]. In addition to purely pragmatic reasons for protecting forests, increased knowledge of forest ecosystem dynamics and relatively high willingness of developed nations to spend resources on nature conservation create a friendly environment for actions aimed at recreating natural forest ecosystems in place of existing forests. Such actions allow for effective protection of forest ecosystem biodiversity and the creation of new habitats that further increase species composition richness [3]. Remote sensing has proven to provide reliable information about spatial distribution of vegetation conditions [6], biophysical properties of plants [7,8], and monitoring of the human impact on valuable habitats [9,10], land cover [11], vegetation communities [12,13], and many other topics
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