Individual Urban Tree Species Classification Using Very High Spatial Resolution Airborne Multi-Spectral Imagery Using Longitudinal Profiles

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.

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Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Remote Sensing ◽

10.3390/rs12183092 ◽

2020 ◽

Vol 12 (18) ◽

pp. 3092 ◽

Cited By ~ 1

Author(s):

Mathieu Varin ◽

Bilel Chalghaf ◽

Gilles Joanisse

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Individual Species ◽

Global Approach ◽

Conifer Species ◽

Object Based ◽

Very High Spatial Resolution ◽

Very High ◽

Tree Type

Species identification in Quebec, Canada, is usually performed with photo-interpretation at the stand level, and often results in a lack of precision which affects forest management. Very high spatial resolution imagery, such as WorldView-3 and Light Detection and Ranging have the potential to overcome this issue. The main objective of this study is to map 11 tree species at the tree level using an object-based approach. For modeling, 240 variables were derived from WorldView-3 with pixel-based and arithmetic feature calculation techniques. A global approach (11 species) was compared to a hierarchical approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were compared: support vector machine, classification and regression tree, random forest (RF), k-nearest neighbors, and linear discriminant analysis. Each model was assessed using 16-band or first 8-band derived variables, with the results indicating higher precision for the RF technique. Higher accuracies were found using 16-band instead of 8-band derived variables for the global approach (overall accuracy (OA): 75% vs. 71%, Kappa index of agreement (KIA): 0.72 vs. 0.67) and tree type level (OA: 99% vs. 97%, KIA: 0.97 vs. 0.95). For broadleaf individual species, higher accuracy was found using first 8-band derived variables (OA: 70% vs. 68%, KIA: 0.63 vs. 0.60). No distinction was found for individual conifer species (OA: 94%, KIA: 0.93). This paper demonstrates that a hierarchical classification approach gives better results for conifer species and that using an 8-band WorldView-3 instead of a 16-band is sufficient.

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Improving the classification of urban tree diversity from Very High Spatial Resolution hyperspectral images: comparison of multiples techniques

2019 Joint Urban Remote Sensing Event (JURSE) ◽

10.1109/jurse.2019.8809030 ◽

2019 ◽

Author(s):

Charlotte Brabant ◽

Emilien Alvarez-Vanhard ◽

Thomas Houet

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Tree Diversity ◽

Hyperspectral Images ◽

Urban Tree ◽

Very High Spatial Resolution ◽

Very High

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SEMANTIC SEGMENTATION OF FOREST STANDS OF PURE SPECIES AS A GLOBAL OPTIMIZATION PROBLEM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-1-w1-141-2017 ◽

2017 ◽

Vol IV-1/W1 ◽

pp. 141-148 ◽

Cited By ~ 2

Author(s):

C. Dechesne ◽

C. Mallet ◽

A. Le Bris ◽

V. Gouet-Brunet

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Semantic Segmentation ◽

Airborne Lidar ◽

Forest Stands ◽

Pure Species ◽

Individual Tree ◽

Very High Spatial Resolution ◽

Very High

Forest stand delineation is a fundamental task for forest management purposes, that is still mainly manually performed through visual inspection of geospatial (very) high spatial resolution images. Stand detection has been barely addressed in the literature which has mainly focused, in forested environments, on individual tree extraction and tree species classification. From a methodological point of view, stand detection can be considered as a semantic segmentation problem. It offers two advantages. First, one can retrieve the dominant tree species per segment. Secondly, one can benefit from existing low-level tree species label maps from the literature as a basis for high-level object extraction. Thus, the semantic segmentation issue becomes a regularization issue in a weakly structured environment and can be formulated in an energetical framework. This papers aims at investigating which regularization strategies of the literature are the most adapted to delineate and classify forest stands of pure species. Both airborne lidar point clouds and multispectral very high spatial resolution images are integrated for that purpose. The local methods (such as filtering and probabilistic relaxation) are not adapted for such problem since the increase of the classification accuracy is below 5%. The global methods, based on an energy model, tend to be more efficient with an accuracy gain up to 15%. The segmentation results using such models have an accuracy ranging from 96% to 99%.

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High spatial resolution photo mosaicking for the monitoring of coralligenous reefs

Coral Reefs ◽

10.1007/s00338-021-02136-4 ◽

2021 ◽

Author(s):

E. Casoli ◽

D. Ventura ◽

G. Mancini ◽

D. S. Pace ◽

A. Belluscio ◽

...

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Low Cost ◽

Pixel Resolution ◽

Recording Apparatus ◽

Assemblage Composition ◽

Very High Spatial Resolution ◽

Vertical Walls ◽

Non Destructive ◽

Very High

AbstractCoralligenous reefs are characterized by large bathymetric and spatial distribution, as well as heterogeneity; in shallow environments, they develop mainly on vertical and sub-vertical rocky walls. Mainly diver-based techniques are carried out to gain detailed information on such habitats. Here, we propose a non-destructive and multi-purpose photo mosaicking method to study and monitor coralligenous reefs developing on vertical walls. High-pixel resolution images using three different commercial cameras were acquired on a 10 m2 reef, to compare the effectiveness of photomosaic method to the traditional photoquadrats technique in quantifying the coralligenous assemblage. Results showed very high spatial resolution and accuracy among the photomosaic acquired with different cameras and no significant differences with photoquadrats in assessing the assemblage composition. Despite the large difference in costs of each recording apparatus, little differences emerged from the assemblage characterization: through the analysis of the three photomosaics twelve taxa/morphological categories covered 97–99% of the sampled surface. Photo mosaicking represents a low-cost method that minimizes the time spent underwater by divers and capable of providing new opportunities for further studies on shallow coralligenous reefs.

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