Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong

Abbas, Sawaid; Pan, Qian; Wong, Man Sing; Li, Zhilin; Wang, Jicheng; Ng, Kathy Tze Kwun; Kwok, Coco Yin Tung; Hui, Karena Ka Wai

doi:10.1016/j.isprsjprs.2021.05.003

Cited by 37 publications

(21 citation statements)

References 48 publications

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“…In the former, the definition of classes and the level of detail may strongly vary depending on the application, where the focus on particular ecosystem services of vegetation is often the determining factor for the proposed typology [12,30,44]. In the case of taxonomy-based mapping, the majority of studies focus on the mapping of tree species or genera (e.g., [66,108]). Very often, these studies are limited to only a few of the most prevailing tree species that occur within an urban area.…”

Section: Discussionmentioning

confidence: 99%

“…Another way to acquire information regarding the phenological profile of different tree species is by using terrestrial imagery taken at specific intervals throughout the year. Abbas et al [108] achieved accuracies of up to 96% for the mapping of 19 tree species with bi-monthly hyperspectral, terrestrial imagery.…”

Section: Importance Of Phenology In Vegetation Mappingmentioning

confidence: 99%

“…The same holds true for the use of LiDAR data. Overall, the use of deep learning techniques appears to be more popular with Street View imagery [98,116] and for species-level mapping [55,108,117].…”

Section: Supervised Learning Approachesmentioning

confidence: 99%

“…The strength of deep learning techniques is based on the capability of a network to attain a feature representation that maximizes the separability between the different classes. Thus far, only a limited number of studies have applied deep learning techniques for the classification of urban green [55,73,98,108,121]. Overall, various groups of deep learning algorithms can be distinguished, of which three have been used in the papers identified in this literature review for mapping urban vegetation: (a) a Boltzman machine, (b) an MLP with more than one hidden layer and (c) a convolutional neural network architecture (CNN).…”

Section: Deep Learningmentioning

confidence: 99%

“…Abbas et al [108] used an MLP with three hidden layers to classify 17 tree species using imagery acquired with a hyperspectral, terrestrial sensor at different moments throughout the year. The model was able to predict species with high accuracy, ranging from 84% to 96% depending on the season.…”

Section: Deep Learningmentioning

confidence: 99%

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Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Neyns

Canters

2022

Remote Sensing

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Green space is increasingly recognized as an important component of the urban environment. Adequate management and planning of urban green space is crucial to maximize its benefits for urban inhabitants and for the urban ecosystem in general. Inventorying urban vegetation is a costly and time-consuming process. The development of new remote sensing techniques to map and monitor vegetation has therefore become an important topic of interest to many scholars. Based on a comprehensive survey of the literature, this review article provides an overview of the main approaches proposed to map urban vegetation from high-resolution remotely sensed data. Studies are reviewed from three perspectives: (a) the vegetation typology, (b) the remote sensing data used and (c) the mapping approach applied. With regard to vegetation typology, a distinction is made between studies focusing on the mapping of functional vegetation types and studies performing mapping of lower-level taxonomic ranks, with the latter mainly focusing on urban trees. A wide variety of high-resolution imagery has been used by researchers for both types of mapping. The fusion of various types of remote sensing data, as well as the inclusion of phenological information through the use of multi-temporal imagery, prove to be the most promising avenues to improve mapping accuracy. With regard to mapping approaches, the use of deep learning is becoming more established, mostly for the mapping of tree species. Through this survey, several research gaps could be identified. Interest in the mapping of non-tree species in urban environments is still limited. The same holds for the mapping of understory species. Most studies focus on the mapping of public green spaces, while interest in the mapping of private green space is less common. The use of imagery with a high spatial and temporal resolution, enabling the retrieval of phenological information for mapping and monitoring vegetation at the species level, still proves to be limited in urban contexts. Hence, mapping approaches specifically tailored towards time-series analysis and the use of new data sources seem to hold great promise for advancing the field. Finally, unsupervised learning techniques and active learning, so far rarely applied in urban vegetation mapping, are also areas where significant progress can be expected.

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Section: Discussionmentioning

confidence: 99%

Section: Importance Of Phenology In Vegetation Mappingmentioning

confidence: 99%

Section: Supervised Learning Approachesmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

Section: Deep Learningmentioning

confidence: 99%

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Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Neyns

Canters

2022

Remote Sensing

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Geospatial Techniques in Sustainable Forest Management for Ecorestoration and Different Environmental Protection Issues

Banerjee

Palit

2023

Ecorestoration for Sustainability

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Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model

Marconi

Graves

Zare

et al. 2023

Remote Sens Ecol Conserv

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Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground‐based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, many classification models only include the most abundant species, leading to biased predictions at broad scales. For example, if only common species are used to train the model, this assumes that these samples are representative across the entire landscape. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. We use a targeted sampling workflow to the Ordway Swisher Biological Station within the US National Ecological Observatory Network (NEON), where traditional forestry plots had identified six canopy tree species with more than 10 individuals at the site. Combining iterative model development with rare species sampling, we extend a training dataset to include 14 species. Using a multi‐temporal hierarchical model, we demonstrate the ability to include species predicted at <1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to 61% accuracy of a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670 000 individual trees. We find distinct patches of forest composed of rarer species at the full‐site scale, highlighting the importance of capturing species diversity in training data. We estimate the relative abundance of 14 species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. For example, we estimate that the dominant species, Pinus palustris accounts for c. 28% of predicted stems, with models predicting a range of counts between 160 000 and 210 000 individuals. These maps provide the first estimates of canopy tree diversity within a NEON site to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales.

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Characterizing and classifying urban tree species using bi-monthly terrestrial hyperspectral images in Hong Kong

Cited by 37 publications

References 48 publications

Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review

Geospatial Techniques in Sustainable Forest Management for Ecorestoration and Different Environmental Protection Issues

Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model

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