CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATA Article Swipe
Zhihui Wang
,
Jianchao Wu
,
Y. Wang
,
Xiangbing Kong
,
Hong Zhe Bao
,
Yongxin Ni
,
Longlong Ma
,
Jiaxin Jin
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlii-3-2629-2018
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-archives-xlii-3-2629-2018
Mapping tree species is essential for sustainable planning as well as to improve our understanding of the role of different trees as different ecological service. However, crown-level tree species automatic classification is a challenging task due to the spectral similarity among diversified tree species, fine-scale spatial variation, shadow, and underlying objects within a crown. Advanced remote sensing data such as airborne Light Detection and Ranging (LiDAR) and hyperspectral imagery offer a great potential opportunity to derive crown spectral, structure and canopy physiological information at the individual crown scale, which can be useful for mapping tree species. In this paper, an innovative approach was developed for tree species classification at the crown level. The method utilized LiDAR data for individual tree crown delineation and morphological structure extraction, and Compact Airborne Spectrographic Imager (CASI) hyperspectral imagery for pure crown-scale spectral extraction. Specifically, four steps were include: 1) A weighted mean filtering method was developed to improve the accuracy of the smoothed Canopy Height Model (CHM) derived from LiDAR data; 2) The marker-controlled watershed segmentation algorithm was, therefore, also employed to delineate the tree-level canopy from the CHM image in this study, and then individual tree height and tree crown were calculated according to the delineated crown; 3) Spectral features within 3 × 3 neighborhood regions centered on the treetops detected by the treetop detection algorithm were derived from the spectrally normalized CASI imagery; 4) The shape characteristics related to their crown diameters and heights were established, and different crown-level tree species were classified using the combination of spectral and shape characteristics. Analysis of results suggests that the developed classification strategy in this paper (OA = 85.12 %, Kc = 0.90) performed better than LiDAR-metrics method (OA = 79.86 %, Kc = 0.81) and spectral-metircs method (OA = 71.26, Kc = 0.69) in terms of classification accuracy, which indicated that the advanced method of data processing and sensitive feature selection are critical for improving the accuracy of crown-level tree species classification.
Related Topics
Concepts
Lidar
Remote sensing
Crown (dentistry)
Hyperspectral imaging
Tree (set theory)
Canopy
Scale (ratio)
Tree canopy
Shadow (psychology)
Segmentation
Environmental science
Computer science
Geography
Artificial intelligence
Mathematics
Cartography
Dentistry
Archaeology
Psychology
Mathematical analysis
Psychotherapist
Medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-archives-xlii-3-2629-2018
- https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2629/2018/isprs-archives-XLII-3-2629-2018.pdf
- OA Status
- diamond
- Cited By
- 1
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2802098100
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2802098100Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-archives-xlii-3-2629-2018Digital Object Identifier
- Title
-
CROWN-LEVEL TREE SPECIES CLASSIFICATION USING INTEGRATED AIRBORNE HYPERSPECTRAL AND LIDAR REMOTE SENSING DATAWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-05-02Full publication date if available
- Authors
-
Zhihui Wang, Jianchao Wu, Y. Wang, Xiangbing Kong, Hong Zhe Bao, Yongxin Ni, Longlong Ma, Jiaxin JinList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-archives-xlii-3-2629-2018Publisher landing page
- PDF URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2629/2018/isprs-archives-XLII-3-2629-2018.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3/2629/2018/isprs-archives-XLII-3-2629-2018.pdfDirect OA link when available
- Concepts
-
Lidar, Remote sensing, Crown (dentistry), Hyperspectral imaging, Tree (set theory), Canopy, Scale (ratio), Tree canopy, Shadow (psychology), Segmentation, Environmental science, Computer science, Geography, Artificial intelligence, Mathematics, Cartography, Dentistry, Archaeology, Psychology, Mathematical analysis, Psychotherapist, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2021: 1Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.according | 200 |
| abstract_inverted_index.accuracy, | 304 |
| abstract_inverted_index.algorithm | 173, 223 |
| abstract_inverted_index.automatic | 30 |
| abstract_inverted_index.delineate | 179 |
| abstract_inverted_index.detection | 222 |
| abstract_inverted_index.developed | 104, 152, 266 |
| abstract_inverted_index.diameters | 240 |
| abstract_inverted_index.different | 20, 23, 246 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.filtering | 149 |
| abstract_inverted_index.improving | 321 |
| abstract_inverted_index.indicated | 306 |
| abstract_inverted_index.performed | 279 |
| abstract_inverted_index.potential | 73 |
| abstract_inverted_index.selection | 317 |
| abstract_inverted_index.sensitive | 315 |
| abstract_inverted_index.spectral, | 78 |
| abstract_inverted_index.structure | 79, 125 |
| abstract_inverted_index.watershed | 171 |
| abstract_inverted_index.calculated | 199 |
| abstract_inverted_index.classified | 251 |
| abstract_inverted_index.delineated | 203 |
| abstract_inverted_index.ecological | 24 |
| abstract_inverted_index.fine-scale | 45 |
| abstract_inverted_index.individual | 86, 119, 192 |
| abstract_inverted_index.innovative | 101 |
| abstract_inverted_index.normalized | 229 |
| abstract_inverted_index.processing | 313 |
| abstract_inverted_index.similarity | 40 |
| abstract_inverted_index.spectrally | 228 |
| abstract_inverted_index.therefore, | 175 |
| abstract_inverted_index.tree-level | 181 |
| abstract_inverted_index.underlying | 50 |
| abstract_inverted_index.variation, | 47 |
| abstract_inverted_index.challenging | 34 |
| abstract_inverted_index.combination | 254 |
| abstract_inverted_index.crown-level | 27, 247, 325 |
| abstract_inverted_index.crown-scale | 137 |
| abstract_inverted_index.delineation | 122 |
| abstract_inverted_index.diversified | 42 |
| abstract_inverted_index.extraction, | 126 |
| abstract_inverted_index.extraction. | 139 |
| abstract_inverted_index.information | 83 |
| abstract_inverted_index.opportunity | 74 |
| abstract_inverted_index.sustainable | 7 |
| abstract_inverted_index.established, | 244 |
| abstract_inverted_index.neighborhood | 212 |
| abstract_inverted_index.segmentation | 172 |
| abstract_inverted_index.LiDAR-metrics | 282 |
| abstract_inverted_index.Specifically, | 140 |
| abstract_inverted_index.hyperspectral | 68, 133 |
| abstract_inverted_index.morphological | 124 |
| abstract_inverted_index.physiological | 82 |
| abstract_inverted_index.understanding | 15 |
| abstract_inverted_index.Spectrographic | 130 |
| abstract_inverted_index.classification | 31, 108, 267, 303 |
| abstract_inverted_index.characteristics | 235 |
| abstract_inverted_index.classification. | 328 |
| abstract_inverted_index.characteristics. | 259 |
| abstract_inverted_index.spectral-metircs | 292 |
| abstract_inverted_index.marker-controlled | 170 |
| cited_by_percentile_year.max | 93 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.47444582 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |