Tree Species Classification of the UNESCO Man and the Biosphere Karkonoski National Park (Poland) Using Artificial Neural Networks and APEX Hyperspectral Images

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.

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An original method for tree species classification using multitemporal multispectral and hyperspectral satellite data

Silva Fennica ◽

10.14214/sf.10143 ◽

2020 ◽

Vol 54 (2) ◽

Cited By ~ 2

Author(s):

Olga Grigorieva ◽

Olga Brovkina ◽

Alisher Saidov

Keyword(s):

Tree Species ◽

Original Method ◽

Hyperspectral Data ◽

Species Classification ◽

Multispectral Data ◽

Spectral Signatures ◽

Tree Species Classification ◽

Temporal Features ◽

Study Sites ◽

Near Future

This study proposes an original method for tree species classification by satellite remote sensing. The method uses multitemporal multispectral (Landsat OLI) and hyperspectral (Resurs-P) data acquired from determined vegetation periods. The method is based on an original database of spectral features taking into account seasonal variations of tree species spectra. Changes in the spectral signatures of forest classes are analyzed and new spectralâtemporal features are created for the classification. Study sites are located in the Czech Republic and northwest (NW) Russia. The differences in spectral reflectance between tree species are shown as statistically significant in the sub-seasons of spring, first half of summer, and main autumn for both study sites. Most of the errors are related to the classification of deciduous species and misclassification of birch as pine (NW Russia site), pine as mixture of pine and spruce, and pine as mixture of spruce and beech (Czech site). Forest species are mapped with accuracy as high as 80% (NW Russia site) and 81% (Czech site). The classification using multitemporal multispectral data has a kappa coefficient 1.7 times higher than does that of classification using a single multispectral image and 1.3 times greater than that of the classification using single hyperspectral images. Potentially, classification accuracy can be improved by the method when applying multitemporal satellite hyperspectral data, such as in using new, near-future products EnMap and/or HyspIRI with high revisit time.

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Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China

Forests ◽

10.3390/f10090818 ◽

2019 ◽

Vol 10 (9) ◽

pp. 818

Author(s):

Yanbiao Xi ◽

Chunying Ren ◽

Zongming Wang ◽

Shiqing Wei ◽

Jialing Bai ◽

...

Keyword(s):

Deep Learning ◽

Tree Species ◽

Classification Accuracy ◽

Learning Algorithms ◽

Spectral Characteristics ◽

Great Influence ◽

Hyperspectral Images ◽

Changbai Mountains ◽

Species Classification ◽

Tree Species Classification

The accurate characterization of tree species distribution in forest areas can help significantly reduce uncertainties in the estimation of ecosystem parameters and forest resources. Deep learning algorithms have become a hot topic in recent years, but they have so far not been applied to tree species classification. In this study, one-dimensional convolutional neural network (Conv1D), a popular deep learning algorithm, was proposed to automatically identify tree species using OHS-1 hyperspectral images. Additionally, the random forest (RF) classifier was applied to compare to the algorithm of deep learning. Based on our experiments, we drew three main conclusions: First, the OHS-1 hyperspectral images used in this study have high spatial resolution (10 m), which reduces the influence of mixed pixel effect and greatly improves the classification accuracy. Second, limited by the amount of sample data, Conv1D-based classifier does not need too many layers to achieve high classification accuracy. In addition, the size of the convolution kernel has a great influence on the classification accuracy. Finally, the accuracy of Conv1D (85.04%) is higher than that of RF model (80.61%). Especially for broadleaf species with similar spectral characteristics, such as Manchurian walnut and aspen, the accuracy of Conv1D-based classifier is significantly higher than RF classifier (87.15% and 71.77%, respectively). Thus, the Conv1D-based deep learning framework combined with hyperspectral imagery can efficiently improve the accuracy of tree species classification and has great application prospects in the future.

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Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images

2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS ◽

10.1109/igarss.2013.6723224 ◽

2013 ◽

Cited By ~ 1

Author(s):

Claudio Persello ◽

Michele Dalponte ◽

Terje Gobakken ◽

Erik Naesset

Keyword(s):

Active Learning ◽

Tree Species ◽

Hyperspectral Images ◽

Sample Collection ◽

Species Classification ◽

Ground Sample ◽

Tree Species Classification

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Tree species classification using airborne hyperspectral data in subtropical mountainous forest

2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) ◽

10.1109/igarss.2016.7729590 ◽

2016 ◽

Author(s):

Wen Jia ◽

Yong Pang ◽

Shili Meng ◽

Hongbo Ju ◽

Zengyuan Li

Keyword(s):

Tree Species ◽

Hyperspectral Data ◽

Species Classification ◽

Tree Species Classification

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Tree Species Classification in Boreal Forests With Hyperspectral Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2012.2216272 ◽

2013 ◽

Vol 51 (5) ◽

pp. 2632-2645 ◽

Cited By ~ 160

Author(s):

Michele Dalponte ◽

Hans Ole Orka ◽

Terje Gobakken ◽

Damiano Gianelle ◽

Erik Naesset

Keyword(s):

Tree Species ◽

Boreal Forests ◽

Hyperspectral Data ◽

Species Classification ◽

Tree Species Classification

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Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Journal of Sensors ◽

10.1155/2019/3247946 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Guang Yang ◽

Yaolong Zhao ◽

Baoxin Li ◽

Yuntao Ma ◽

Ruren Li ◽

...

Keyword(s):

Tree Species ◽

Vegetation Indices ◽

Airborne Lidar ◽

Hyperspectral Images ◽

Support Vector ◽

Textural Features ◽

Integrated Sensors ◽

Species Classification ◽

Multiple Features ◽

Tree Species Classification

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

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