Classification of tree species in a heterogeneous urban environment using object-based ensemble analysis and World View-2 satellite imagery

2021 ◽  
Author(s):  
Simbarashe Jombo ◽  
Elhadi Adam ◽  
John Odindi
Keyword(s):  
Urban Environment ◽  
Satellite Imagery ◽  
Tree Species ◽  
World View ◽  
Object Based ◽  
Ensemble Analysis

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.

Mapping changes in the largest continuous Amazonian mangrove belt using object-based classification of multisensor satellite imagery

2013 ◽  
Vol 117 ◽  
pp. 83-93 ◽  
Author(s):  
Wilson R. Nascimento ◽  
Pedro Walfir M. Souza-Filho ◽  
Christophe Proisy ◽  
Richard M. Lucas ◽  
Ake Rosenqvist
Keyword(s):  
Satellite Imagery ◽  
Object Based

scholarly journals Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset

2020 ◽  
Vol 12 (15) ◽  
pp. 2475 ◽  
Author(s):  
Daniel S. W. Katz ◽  
Stuart A. Batterman ◽  
Shannon J. Brines
Keyword(s):  
Classification Accuracy ◽  
World View ◽  
Urban Trees ◽  
Aerial Images ◽  
Aerial Image ◽  
Urban Tree ◽  
Object Based ◽  
Street Tree ◽  
Multi Temporal

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.

Pixel- and object-based multispectral classification of forest tree species from small unmanned aerial vehicles

2018 ◽  
Vol 6 (4) ◽  
pp. 195-211 ◽  
Author(s):  
Steven E. Franklin
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Tree Species ◽  
Forest Inventory ◽  
Classification Accuracy ◽  
Forest Tree ◽  
Tree Crown ◽  
Species Classification ◽  
Aerial Vehicles ◽  
Object Based

Forest inventory, monitoring, and assessment requires accurate tree species identification and mapping. Recent experiences with multispectral data from small fixed-wing and rotary blade unmanned aerial vehicles (UAVs) suggest a role for this technology in the emerging paradigm of enhanced forest inventory (EFI). In this paper, pixel-based and object-based image analysis (OBIA) methods were compared in UAV-based tree species classification of nine commercial tree species in mature eastern Ontario mixedwood forests. Unsupervised clustering and supervised classification of tree crown pixels yielded approximately 50%–60% classification accuracy overall; OBIA with image segmentation to delineate tree crowns and machine learning yielded up to 80% classification accuracy overall. Spectral response patterns and tree crown shape and geometric differences were interpreted in context of their ability to separate tree species of interest with these classification methods. Accuracy assessment was based on field-based forest inventory tree species identification. The paper provides a brief summary of future research issues that will influence the growth of this geomatics innovation in forest tree species classification and forest inventory.

scholarly journals Classification of tree species composition using a combination of multispectral imagery and airborne laser scanning data

2017 ◽  
Vol 63 (1) ◽  
pp. 1-9 ◽  
Author(s):  
Maroš Sedliak ◽  
Ivan Sačkov ◽  
Ladislav Kulla
Keyword(s):  
Species Composition ◽  
Tree Species ◽  
Laser Scanning ◽  
Airborne Laser Scanning ◽  
Tree Species Composition ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Individual Trees

AbstractRemote Sensing provides a variety of data and resources useful in mapping of forest. Currently, one of the common applications in forestry is the identification of individual trees and tree species composition, using the object-based image analysis, resulting from the classification of aerial or satellite imagery. In the paper, there is presented an approach to the identification of group of tree species (deciduous - coniferous trees) in diverse structures of close-to-nature mixed forests of beech, fir and spruce managed by selective cutting. There is applied the object-oriented classification based on multispectral images with and without the combination with airborne laser scanning data in the eCognition Developer 9 software. In accordance to the comparison of classification results, the using of the airborne laser scanning data allowed identifying ground of terrain and the overall accuracy of classification increased from 84.14% to 87.42%. Classification accuracy of class “coniferous” increased from 82.93% to 85.73% and accuracy of class “deciduous” increased from 84.79% to 90.16%.

scholarly journals Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1419 ◽  
Author(s):  
Mathias Wessel ◽  
Melanie Brandmeier ◽  
Dirk Tiede
Keyword(s):  
Machine Learning ◽  
Tree Species ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Inventory Data ◽  
Oak Trees ◽  
Object Based ◽  
Sentinel 2

We use freely available Sentinel-2 data and forest inventory data to evaluate the potential of different machine-learning approaches to classify tree species in two forest regions in Bavaria, Germany. Atmospheric correction was applied to the level 1C data, resulting in true surface reflectance or bottom of atmosphere (BOA) output. We developed a semiautomatic workflow for the classification of deciduous (mainly spruce trees), beech and oak trees by evaluating different classification algorithms (object- and pixel-based) in an architecture optimized for distributed processing. A hierarchical approach was used to evaluate different band combinations and algorithms (Support Vector Machines (SVM) and Random Forest (RF)) for the separation of broad-leaved vs. coniferous trees. The Ebersberger forest was the main project region and the Freisinger forest was used in a transferability study. Accuracy assessment and training of the algorithms was based on inventory data, validation was conducted using an independent dataset. A confusion matrix, with User´s and Producer´s Accuracies, as well as Overall Accuracies, was created for all analyses. In total, we tested 16 different classification setups for coniferous vs. broad-leaved trees, achieving the best performance of 97% for an object-based multitemporal SVM approach using only band 8 from three scenes (May, August and September). For the separation of beech and oak trees we evaluated 54 different setups, the best result achieved an accuracy of 91% for an object-based, SVM, multitemporal approach using bands 8, 2 and 3 of the May scene for segmentation and all principal components of the August scene for classification. The transferability of the model was tested for the Freisinger forest and showed similar results. This project points out that Sentinel-2 had only marginally worse results than comparable commercial high-resolution satellite sensors and is well-suited for forest analysis on a tree-stand level.

Assessment of Salt Marsh Change on Assateague Island National Seashore Between 1962 and 2016

2020 ◽  
Vol 86 (3) ◽  
pp. 187-194 ◽  
Author(s):  
Anthony Campbell ◽  
Yeqiao Wang
Keyword(s):  
Salt Marsh ◽  
Satellite Imagery ◽  
Salt Marshes ◽  
Wave Attenuation ◽  
Change Analysis ◽  
Object Based ◽  
Island System ◽  
High Resolution Satellite Imagery ◽  
Assateague Island

Salt marshes provide extensive ecosystem services, including high biodiversity, denitrification, and wave attenuation. In the mid-Atlantic, sea level rise is predicted to affect salt marsh ecosystems severely. This study mapped the entirety of Assateague Island with Very High Resolution satellite imagery and object-based methods to determine an accurate salt marsh baseline for change analysis. Topobathy-metric light detection and ranging was used to map the salt marsh and model expected tidal effects. The satellite imagery, collected in 2016 and classified at two hierarchical thematic schemes, were compared to determine appropriate thematic richness. Change analysis between this 2016 map and both a manually delineated 1962 salt marsh extent and image classification of the island from 1994 determined rates off change. The study found that from 1962 to 1994, salt marsh expanded by 4.01 ha/year, and from 1994 to 2016 salt marsh was lost at a rate of -3.4 ha/ year. The study found that salt marsh composition, (percent vegetated salt marsh) was significantly influenced by elevation, the length of mosquito ditches, and starting salt marsh composition. The study illustrates the importance of remote sensing monitoring for understanding site-specific changes to salt marsh environments and the barrier island system.

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