scholarly journals Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

2011 ◽  
Vol 3 (10) ◽  
pp. 2222-2242 ◽  
Author(s):  
Muhammad Kamal ◽  
Stuart Phinn
Keyword(s):  
Hyperspectral Data ◽  
Mangrove Species ◽  
Object Based ◽  
Species Mapping

scholarly journals GF-5 Hyperspectral Data for Species Mapping of Mangrove in Mai Po, Hong Kong

2020 ◽  
Vol 12 (4) ◽  
pp. 656 ◽  
Author(s):  
Luoma Wan ◽  
Yinyi Lin ◽  
Hongsheng Zhang ◽  
Feng Wang ◽  
Mingfeng Liu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Spectral Resolution ◽  
Hyperspectral Data ◽  
Support Vector ◽  
Species Discrimination ◽  
Infrared Range ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Spectral Bands ◽  
Species Mapping

Hyperspectral data has been widely used in species discrimination of plants with rich spectral information in hundreds of spectral bands, while the availability of hyperspectral data has hindered its applications in many specific cases. The successful operation of the Chinese satellite, Gaofen-5 (GF-5), provides potentially promising new hyperspectral dataset with 330 spectral bands in visible and near infrared range. Therefore, there is much demand for assessing the effectiveness and superiority of GF-5 hyperspectral data in plants species mapping, particularly mangrove species mapping, to better support the efficient mangrove management. In this study, mangrove forest in Mai Po Nature Reserve (MPNR), Hong Kong was selected as the study area. Four dominant native mangrove species were investigated in this study according to the field surveys. Two machine learning methods, Random Forests and Support Vector Machines, were employed to classify mangrove species with Landsat 8, Simulated Hyperion and GF-5 data sets. The results showed that 97 more bands of GF-5 over Hyperion brought a higher over accuracy of 87.12%, in comparison with 86.82% from Hyperion and 73.89% from Landsat 8. The higher spectral resolution of 5 nm in GF-5 was identified as making the major contribution, especially for the mapping of Aegiceras corniculatum. Therefore, GF-5 is likely to improve the classification accuracy of mangrove species mapping via enhancing spectral resolution and thus has promising potential to improve mangrove monitoring at species level to support mangrove management.

Urban Tree Species Mapping Using Airborne LiDAR and Hyperspectral Data

2016 ◽  
Vol 44 (4) ◽  
pp. 595-603 ◽  
Author(s):  
Yuanyong Dian ◽  
Yong Pang ◽  
Yanfang Dong ◽  
Zengyuan Li
Keyword(s):  
Tree Species ◽  
Hyperspectral Data ◽  
Airborne Lidar ◽  
Urban Tree ◽  
Species Mapping

scholarly journals Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data

2021 ◽  
Vol 179 ◽  
pp. 35-49
Author(s):  
Laura Elena Cué La Rosa ◽  
Camile Sothe ◽  
Raul Queiroz Feitosa ◽  
Cláudia Maria de Almeida ◽  
Marcos Benedito Schimalski ◽  
...  
Keyword(s):  
Tree Species ◽  
Hyperspectral Data ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Species Mapping

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

2019 ◽  
Vol 11 (3) ◽  
pp. 254 ◽  
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.

Tropical mangrove species discrimination using hyperspectral data: A laboratory study

2005 ◽  
Vol 65 (1-2) ◽  
pp. 371-379 ◽  
Author(s):  
Chaichoke Vaiphasa ◽  
Suwit Ongsomwang ◽  
Tanasak Vaiphasa ◽  
Andrew K. Skidmore
Keyword(s):  
Laboratory Study ◽  
Hyperspectral Data ◽  
Species Discrimination ◽  
Mangrove Species
Sign in / Sign up

Export Citation Format

Share Document