scholarly journals Object-Based Tree Species Classification in Urban Ecosystems Using LiDAR and Hyperspectral Data

Forests ◽  
2016 ◽  
Vol 7 (12) ◽  
pp. 122 ◽  
Author(s):  
Zhongya Zhang ◽  
Alexandra Kazakova ◽  
Ludmila Moskal ◽  
Diane Styers
Keyword(s):  
Tree Species ◽  
Urban Ecosystems ◽  
Hyperspectral Data ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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

Silva Fennica ◽  
2020 ◽  
Vol 54 (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 spectral–temporal 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.

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

2018 ◽  
Vol 10 (7) ◽  
pp. 1111 ◽  
Author(s):  
Edwin Raczko ◽  
Bogdan Zagajewski
Keyword(s):  
Tree Species ◽  
National Park ◽  
Accuracy Assessment ◽  
Research Area ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Species Classification ◽  
Tree Species Composition ◽  
Tree Species Classification ◽  
R Programming

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.

Tree Species Classification in Boreal Forests With Hyperspectral Data

2013 ◽  
Vol 51 (5) ◽  
pp. 2632-2645 ◽  
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

scholarly journals Tree Species Classification in a Highly Diverse Subtropical Forest Integrating UAV-Based Photogrammetric Point Cloud and Hyperspectral Data

2019 ◽  
Vol 11 (11) ◽  
pp. 1338 ◽  
Author(s):  
Camile Sothe ◽  
Michele Dalponte ◽  
Cláudia Maria de Almeida ◽  
Marcos Benedito Schimalski ◽  
Carla Luciane Lima ◽  
...  
Keyword(s):  
Tree Species ◽  
Point Cloud ◽  
Species Recognition ◽  
Subtropical Forest ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Svm Classifier ◽  
Kappa Index ◽  
Species Classification ◽  
Tree Species Classification

The use of remote sensing data for tree species classification in tropical forests is still a challenging task, due to their high floristic and spectral diversity. In this sense, novel sensors on board of unmanned aerial vehicle (UAV) platforms are a rapidly evolving technology that provides new possibilities for tropical tree species mapping. Besides the acquisition of high spatial and spectral resolution images, UAV-hyperspectral cameras operating in frame format enable to produce 3D hyperspectral point clouds. This study investigated the use of UAV-acquired hyperspectral images and UAV-photogrammetric point cloud (PPC) for classification of 12 major tree species in a subtropical forest fragment in Southern Brazil. Different datasets containing hyperspectral visible/near-infrared (VNIR) bands, PPC features, canopy height model (CHM), and other features extracted from hyperspectral data (i.e., texture, vegetation indices-VIs, and minimum noise fraction-MNF) were tested using a support vector machine (SVM) classifier. The results showed that the use of VNIR hyperspectral bands alone reached an overall accuracy (OA) of 57% (Kappa index of 0.53). Adding PPC features to the VNIR hyperspectral bands increased the OA by 11%. The best result was achieved combining VNIR bands, PPC features, CHM, and VIs (OA of 72.4% and Kappa index of 0.70). When only the CHM was added to VNIR bands, the OA increased by 4.2%. Among the hyperspectral features, besides all the VNIR bands and the two VIs (NDVI and PSSR), the first four MNF features and the textural mean of 565 and 679 nm spectral bands were pointed out as more important to discriminate the tree species according to Jeffries–Matusita (JM) distance. The SVM method proved to be a good classifier for the tree species recognition task, even in the presence of a high number of classes and a small dataset.

Object-based fusion for urban tree species classification from hyperspectral, panchromatic and nDSM data

2019 ◽  
Vol 40 (14) ◽  
pp. 5339-5365 ◽  
Author(s):  
Josselin Aval ◽  
Sophie Fabre ◽  
Emmanuel Zenou ◽  
David Sheeren ◽  
Mathieu Fauvel ◽  
...  
Keyword(s):  
Tree Species ◽  
Species Classification ◽  
Urban Tree ◽  
Tree Species Classification ◽  
Object Based
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