scholarly journals TIME SERIES OF IMAGES TO IMPROVE TREE SPECIES CLASSIFICATION

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 123-128 ◽  
Author(s):  
G. T. Miyoshi ◽  
N. N. Imai ◽  
M. V. A. de Moraes ◽  
A. M. G. Tommaselli ◽  
R. Näsi
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Imaging Spectroscopy ◽  
Forest Monitoring ◽  
Data Sets ◽  
Species Classification ◽  
Spectral Angle Mapper ◽  
Tree Species Classification ◽  
Layer Stacking ◽  
Floristic Variation

Tree species classification provides valuable information to forest monitoring and management. The high floristic variation of the tree species appears as a challenging issue in the tree species classification because the vegetation characteristics changes according to the season. To help to monitor this complex environment, the imaging spectroscopy has been largely applied since the development of miniaturized sensors attached to Unmanned Aerial Vehicles (UAV). Considering the seasonal changes in forests and the higher spectral and spatial resolution acquired with sensors attached to UAV, we present the use of time series of images to classify four tree species. The study area is an Atlantic Forest area located in the western part of São Paulo State. Images were acquired in August 2015 and August 2016, generating three data sets of images: only with the image spectra of 2015; only with the image spectra of 2016; with the layer stacking of images from 2015 and 2016. Four tree species were classified using Spectral angle mapper (SAM), Spectral information divergence (SID) and Random Forest (RF). The results showed that SAM and SID caused an overfitting of the data whereas RF showed better results and the use of the layer stacking improved the classification achieving a kappa coefficient of 18.26 %.

scholarly journals Tree Species Classification in Temperate Forests Using Formosat-2 Satellite Image Time Series

2016 ◽  
Vol 8 (9) ◽  
pp. 734 ◽  
Author(s):  
David Sheeren ◽  
Mathieu Fauvel ◽  
Veliborka Josipović ◽  
Maïlys Lopes ◽  
Carole Planque ◽  
...  
Keyword(s):  
Time Series ◽  
Tree Species ◽  
Temperate Forests ◽  
Satellite Image ◽  
Species Classification ◽  
Tree Species Classification

scholarly journals EVALUATION OF TREE SPECIES CLASSIFICATION METHODS USING MULTI-TEMPORAL SATELLITE IMAGES

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 79-82
Author(s):  
A. Saha ◽  
S. Sastry ◽  
V. A. Dave ◽  
R. Ghosh
Keyword(s):  
Tree Species ◽  
Mean Shift ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Forest Monitoring ◽  
Species Classification ◽  
Classification Techniques ◽  
Tree Species Classification ◽  
Multi Temporal ◽  
Dynamic Time

Abstract. Tree species classification is an important step towards forest monitoring and biodiversity conservation. This research study evaluates several multispectral image classification techniques for tree species over Ahwa village in Dang district, South Gujarat, India. Multispectral images consisting of 4 bands-R, G, B and NIR collected over 4 months was used. Object-based segmentation using mean shift, cluster-based using K-Means and Gaussian Mixture Model (GMM) and pixel-based methods have been analyzed. Additionally, a new method of classification has been described using the Dynamic Time Warping (DTW) algorithm. It outperformed supervised classification techniques with accuracy over 95%. The GMM+DTW model accurately reflected the actual species distribution found in the ground truth.

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 Individual Tree Species Classification by Illuminated—Shaded Area Separation

2009 ◽  
Vol 2 (1) ◽  
pp. 19-35 ◽  
Author(s):  
Eetu Puttonen ◽  
Paula Litkey ◽  
Juha Hyyppä
Keyword(s):  
Tree Species ◽  
Species Classification ◽  
Individual Tree ◽  
Tree Species Classification
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