Combining UAV-based hyperspectral and LiDAR data for mangrove species classification using the rotation forest algorithm

Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach.

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An Internal Crown Geometric Model for Conifer Species Classification With High-Density LiDAR Data

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2656152 ◽

2017 ◽

Vol 55 (5) ◽

pp. 2924-2940 ◽

Cited By ~ 13

Author(s):

Aravind Harikumar ◽

Francesca Bovolo ◽

Lorenzo Bruzzone

Keyword(s):

Geometric Model ◽

High Density ◽

Lidar Data ◽

Conifer Species ◽

Species Classification

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Individual tree species classification using structure features from high density airborne lidar data

2010 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2010.5651629 ◽

2010 ◽

Cited By ~ 1

Author(s):

Jili Li ◽

Baoxin Hu ◽

Gunho Sohn ◽

Linhai Jing

Keyword(s):

Tree Species ◽

High Density ◽

Airborne Lidar ◽

Lidar Data ◽

Species Classification ◽

Individual Tree ◽

Tree Species Classification ◽

Airborne Lidar Data

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FUSION OF LIDAR AND HYPERSPECTRAL DATA FOR SEMANTIC SEGMENTATION OF FOREST TREE SPECIES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-487-2020 ◽

2020 ◽

Vol XLIII-B3-2020 ◽

pp. 487-494

Author(s):

E. Tusa ◽

J. M. Monnet ◽

J. B. Barré ◽

M. Dalla Mura ◽

J. Chanussot

Keyword(s):

Tree Species ◽

Semantic Segmentation ◽

Forest Tree ◽

Feature Reduction ◽

Lidar Data ◽

Topographic Effects ◽

Species Classification ◽

Forest Tree Species ◽

Tree Species Classification ◽

Feature Descriptors

Abstract. Hyperspectral images (HI) and Light Detection and Ranging (LiDAR) provide high resolution radiometric and geometric information for monitoring forests at individual tree crown (ITC) level. It has many important applications for sustainable forest management, biodiversity assessment and healthy ecosystem preservation. However, the integration of different remote sensing modalities is a challenging task for tree species classification due to different artifacts such as the lighting variability, the topographic effects and the atmospheric conditions of the data acquisition. The characterization of ITC can benefit from the extraction and selection of robust feature descriptors that solve these issues. This paper aims to investigate the integration of feature descriptors from HI and LiDAR by using the intra-set and inter-set feature importance for the semantic segmentation of forest tree species. A fusion methodology is proposed between high-density LiDAR data – (20 pulses m−2) and VNIR HI – (160 bands and 0.80 m spatial resolution) acquired on French temperate forests along an altitude gradient. The proposed scheme has three inputs: the field inventory information, the HI and the LiDAR data. Our approach can be described in nine stages: polygon projection, non-overlapping pixel selection, vegetation and shadow removal, LiDAR feature extraction, height mask, robust PCA (rPCA), feature reduction and classification. The overall accuracy of tree species classification at pixel-level was 68.9% by using random forest (RF) classifier. Our approach showed that 74.0% of trees were correctly assigned overall, by having conifer species such as Norway Spruce (Picea abies) with a producer’s accuracy of 97.4%.

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Continuous Wavelet Analysis of Leaf Reflectance Improves Classification Accuracy of Mangrove Species

Remote Sensing ◽

10.3390/rs11030254 ◽

2019 ◽

Vol 11 (3) ◽

pp. 254 ◽

Cited By ~ 3

Author(s):

Yi Xu ◽

Junjie Wang ◽

Anquan Xia ◽

Kangyong Zhang ◽

Xuanyan Dong ◽

...

Keyword(s):

Wavelet Analysis ◽

Classification Accuracy ◽

Vegetation Index ◽

Hyperspectral Data ◽

Continuous Wavelet ◽

Mangrove Forests ◽

Bruguiera Gymnorrhiza ◽

Species Classification ◽

Successive Projections Algorithm ◽

Mangrove Species

Due to continuous degradation of mangrove forests, the accurate monitoring of spatial distribution and species composition of mangroves is essential for restoration, conservation and management of coastal ecosystems. With leaf hyperspectral reflectance, this study aimed to explore the potential of continuous wavelet analysis (CWA) combined with different sample subset partition (stratified random sampling (STRAT), Kennard-Stone sampling algorithm (KS), and sample subset partition based on joint X-Y distances (SPXY)) and feature extraction methods (principal component analysis (PCA), successive projections algorithm (SPA), and vegetation index (VI)) in mangrove species classification. A total of 301 mangrove leaf samples with four species (Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovate and Aegiceras corniculatum) were collected across six different regions. The smoothed reflectance (Smth) and first derivative reflectance (Der) spectra were subjected to CWA using different wavelet scales, and a total of 270 random forest classification models were established and compared. Among the 120 models with CWA of Smth, 88.3% of models increased the overall accuracy (OA) values with an improvement of 0.2–28.6% compared to the model with the Smth spectra; among the 120 models with CWA of Der, 25.8% of models increased the OA values with an improvement of 0.1–11.4% compared to the model with the Der spectra. The model with CWA of Der at the scale of 23 coupling with STRAT and SPA achieved the best classification result (OA = 98.0%), while the best model with Smth and Der alone had OA values of 86.3% and 93.0%, respectively. Moreover, the models using STRAT outperformed those using KS and SPXY, and the models using PCA and SPA had better performances than those using VIs. We have concluded that CWA with suitable scales holds great potential in improving the classification accuracy of mangrove species, and that STRAT combined with the PCA or SPA method is also recommended to improve classification performance. These results may lay the foundation for further studies with UAV-acquired or satellite hyperspectral data, and the encouraging performance of CWA for mangrove species classification can also be extended to other plant species.

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