Exploring Data Mining Techniques for Tree Species Classification Using Co-Registered LiDAR and Hyperspectral Data

NASA Goddard’s LiDAR, Hyperspectral, and Thermal imager provides co-registered remote sensing data on experimental forests. Data mining methods were used to achieve a final tree species classification accuracy of 68% using a combined LiDAR and hyperspectral dataset, and show promise for addressing deforestation and carbon sequestration on a species-specific level

Urban tree classification using discrete-return LiDAR and an object-level local binary pattern algorithm

© 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group. Urban trees have the potential to mitigate some of the harm brought about by rapid urbanization and population growth, as well as serious environmental degradation (e.g. soil erosion, carbon pollution and species extirpation), in cities. This paper presents a novel urban tree extraction modelling approach that uses discrete laser scanning point clouds and object-based textural analysis to (1) develop a model characterised by four sub-models, including (a) height-based split segmentation, (b) feature extraction, (c) texture analysis and (d) classification, and (2) apply this model to classify urban trees. The canopy height model is integrated with the object-level local binary pattern algorithm (LBP) to achieve high classification accuracy. The results of each sub-model reveal that the classification of urban trees based on the height at 47.14 (high) and 2.12 m (low), respectively, while based on crown widths were highest and lowest at 22.5 and 2.55 m, respectively. Results also indicate that the proposed algorithm of urban tree modelling is effective for practical use

UAV-based photogrammetric point clouds and hyperspectral imaging for mapping biodiversity indicators in boreal forests

Peer reviewe

Mapping urban tree species in a tropical environment using airborne multispectral and LiDAR data

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesAccurate and up-to-date urban tree inventory is an essential resource for the development of strategies towards sustainable urban planning, as well as for effective management and preservation of biodiversity. Trees contribute to thermal comfort within urban centers by lessening heat island effect and have a direct impact in the reduction of air pollution. However, mapping individual trees species normally involves time-consuming field work over large areas or image interpretation performed by specialists. The integration of airborne LiDAR data with high-spatial resolution and multispectral aerial image is an alternative and effective approach to differentiate tree species at the individual crown level. This thesis aims to investigate the potential of such remotely sensed data to discriminate 5 common urban tree species using traditional Machine Learning classifiers (Random Forest, Support Vector Machine, and k-Nearest Neighbors) in the tropical environment of Salvador, Brazil. Vegetation indices and texture information were extracted from multispectral imagery, and LiDAR-derived variables for tree crowns, were tested separately and combined to perform tree species classification applying three different classifiers. Random Forest outperformed the other two classifiers, reaching overall accuracy of 82.5% when using combined multispectral and LiDAR data. The results indicate that (1) given the similarity in spectral signature, multispectral data alone is not sufficient to distinguish tropical tree species (only k-NN classifier could detect all species); (2) height values and intensity of crown returns points were the most relevant LiDAR features, combination of both datasets improved accuracy up to 20%; (3) generation of canopy height model derived from LiDAR point cloud is an effective method to delineate individual tree crowns in a semi-automatic approach