A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species

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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Deep Learning Approaches for the Mapping of Tree Species Diversity in a Tropical Wetland Using Airborne LiDAR and High-Spatial-Resolution Remote Sensing Images

Forests ◽

10.3390/f10111047 ◽

2019 ◽

Vol 10 (11) ◽

pp. 1047 ◽

Cited By ~ 5

Author(s):

Ying Sun ◽

Jianfeng Huang ◽

Zurui Ao ◽

Dazhao Lao ◽

Qinchuan Xin

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Species Diversity ◽

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Diversity Index ◽

Tree Species Diversity ◽

Individual Tree ◽

Rgb Images

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity.

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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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Assessment of urban tree shade using fused LIDAR and high spatial resolution imagery

2009 Joint Urban Remote Sensing Event ◽

10.1109/urs.2009.5137619 ◽

2009 ◽

Cited By ~ 1

Author(s):

Thoreau Rory Tooke ◽

Nicholas C. Coops ◽

James A. Voogt

Keyword(s):

Spatial Resolution ◽

High Spatial Resolution ◽

Urban Tree ◽

High Spatial Resolution Imagery

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Predicting fine-scale tree species abundance patterns using biotic variables derived from LiDAR and high spatial resolution imagery

Remote Sensing of Environment ◽

10.1016/j.rse.2014.04.026 ◽

2014 ◽

Vol 150 ◽

pp. 120-131 ◽

Cited By ~ 35

Author(s):

Karin Y. van Ewijk ◽

Christophe F. Randin ◽

Paul M. Treitz ◽

Neal A. Scott

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Species Abundance ◽

Fine Scale ◽

Abundance Patterns ◽

High Spatial Resolution Imagery

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A Deep Fusion uNet for Mapping Forests at Tree Species Levels with Multi-Temporal High Spatial Resolution Satellite Imagery

Remote Sensing ◽

10.3390/rs13183613 ◽

2021 ◽

Vol 13 (18) ◽

pp. 3613

Author(s):

Ying Guo ◽

Zengyuan Li ◽

Erxue Chen ◽

Xu Zhang ◽

Lei Zhao ◽

...

Keyword(s):

Spatial Resolution ◽

Tree Species ◽

High Spatial Resolution ◽

Forest Type ◽

Species Level ◽

Forest Inventories ◽

Forest Classification ◽

Plantation Species ◽

Multi Temporal ◽

Artificial Intelligence Technology

It is critical to acquire the information of forest type at the tree species level due to its strong links with various quantitative and qualitative indicators in forest inventories. The efficiency of deep-learning classification models for high spatial resolution (HSR) remote sensing image has been demonstrated with the ongoing development of artificial intelligence technology. However, due to limited statistical separability and complicated circumstances, completely automatic and highly accurate forest type mapping at the tree species level remains a challenge. To deal with the problem, a novel deep fusion uNet model was developed to improve the performance of forest classification refined at the dominant tree species level by combining the beneficial phenological characteristics of the multi-temporal imagery and the powerful features of the deep uNet model. The proposed model was built on a two-branch deep fusion architecture with the deep Res-uNet model functioning as its backbone. Quantitative assessments of China’s Gaofen-2 (GF-2) HSR satellite data revealed that the suggested model delivered a competitive performance in the Wangyedian forest farm, with an overall classification accuracy (OA) of 93.30% and a Kappa coefficient of 0.9229. The studies also yielded good results in the mapping of plantation species such as the Chinese pine and the Larix principis.

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