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 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 with Multi-Temporal Sentinel-2 Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1794 ◽  
Author(s):  
Magnus Persson ◽  
Eva Lindberg ◽  
Heather Reese
Keyword(s):  
Variable Selection ◽  
Tree Species ◽  
Pedunculate Oak ◽  
Species Classification ◽  
Data Set ◽  
Tree Species Classification ◽  
Red Edge ◽  
Lower Accuracy ◽  
Multi Temporal ◽  
Sentinel 2

The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix × marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user’s accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.

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

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.

Sign in / Sign up

Export Citation Format

Share Document