Leaf area measurement of green wall plants using deep learning with terrestrial laser
Green wall plants have various functionality such as mitigating heat island, creating green space, and absorbing carbon in an urban area. A method to evaluate the various functionality is needed for construction field. This study focuses on the plant structure and deep learning was introduced to est...
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| Published in | Journal of the Japanese Society of Revegetation Technology Vol. 48; no. 1; pp. 9 - 14 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
Tokyo
JAPANESE SOCIETY OF REVEGETATION TECHNOLOGY
31.08.2022
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0916-7439 0916-7439 |
| DOI | 10.7211/jjsrt.48.9 |
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| Abstract | Green wall plants have various functionality such as mitigating heat island, creating green space, and absorbing carbon in an urban area. A method to evaluate the various functionality is needed for construction field. This study focuses on the plant structure and deep learning was introduced to estimate the leaf area and counting leaves automatically. To achieve this, the panorama distant image was created from 3D data acquired by terrestrial laser to prepare for the deep learning model. The model for the presence or absence of leaves had 90% accuracy and the counting leaves had 72% accuracy and leaf area estimated through deep learning had 27% error compared to the destructively sampled data. |
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| AbstractList | Green wall plants have various functionality such as mitigating heat island, creating green space, and absorbing carbon in an urban area. A method to evaluate the various functionality is needed for construction field. This study focuses on the plant structure and deep learning was introduced to estimate the leaf area and counting leaves automatically. To achieve this, the panorama distant image was created from 3D data acquired by terrestrial laser to prepare for the deep learning model. The model for the presence or absence of leaves had 90% accuracy and the counting leaves had 72% accuracy and leaf area estimated through deep learning had 27% error compared to the destructively sampled data. |
| Author | YAMAGUCHI, Jun KATO, Akira ASANO, Ryota KURIKI, Shigeru OSHIMA, Kaori HIKOSAKA, Shoko UEYANAGI, Ryohei |
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| Copyright | 2022 Japanese Society of Revegetation Technology Copyright Japan Science and Technology Agency 2022 |
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| References | 7) 加藤 顕・田村 太壱・市橋 新・小林 達明・高橋 輝昌 (2019) 地上レーザーを用いた階層構造と植被率の自動解析手法. 日本緑化工学会誌, 45(1): 121-126 8) Ki, D., and Lee, S. (2021) Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning, 205: 103920 4) Hirose, T. (2005) Development of the Monsi-Saeki theory on canopy structure and function. Annals of Botany, 95(3): 483-494 21) Yin, H., Kong F., Middel, A., Dronova, I., Xu, H., James P. (2017) Cooling effect of direct green façades during hot summer days: An observational study in Nanjing, China using TIR and 3DPC data. Building and Environment, 116: 195e206 3) 彦坂幸毅 (2016) 植物の光合成・物質生産の測定とモデリング. 共立出版, pp. 30-33 6) 加藤 顕,・安藤 祐樹・吉田 俊也・梶原 康司・本多 嘉明・小林 達明 (2014) 簡易型地上レーザーを用いた毎木調査法. 日本緑化工学会誌, 40(1): 136-141 13) 森本幸裕・小林達明 (2007) 最新 環境緑化工学. 朝倉書店 19) 鈴木弘孝・三坂育正・田代順孝 (2007) 蒸散効率を指標とした壁面緑化の蒸散特性. ランドスケープ研究, 70(5): 401-406 12) Moorthy, I., Miller, J.R., Berni, J.A.J., Zarco-Tejada, P., Hu, B., Chen, J. (2011) Field characterization of olive (Oleaeuropaea L.) tree crown architecture using terrestrial laser scanning data, Agricultural Forest. Meteorology. 151: 204e214 10) Kong, F.H., Yan, W.J., Zheng, G., Yin, H.W., Cavan, G., Zhan, W.F., Zhang, N. Cheng, L. (2016) Retrieval of three-dimensional tree canopy and shade using terrestrial laser scanning (TLS) data to analyze the cooling effect of vegetation. Agricultural Forest Meteorology, 217: 22e34 2) Goodfellow, I., Bengio,Y., and Courville, A. (2016) Deep Learning. The MIT Press 16) Sony Neural Network Console, https://dl.sony.com/ja/ （参照: 2022年2月7日） 17) 鈴木弘孝・三坂育正・村野直康・田代順孝 (2005) 壁面緑化による建物外部の温熱環境改善効果に関する研究. ランドスケープ研究, 68(2): 503-508 20) 竹中優揮・村江行忠・栗木 茂・伊藤 優・市川勇太 (2021) 環境配慮型事務所建築に関する研究（第1報）カーボンマイナスを目指したグリーンオフィス棟の概要. 戸田建設株式会社技術研究報告, 47: 1-4 22) 渡部俊太郎・大西信徳・皆川まり・伊勢武史 (2020) 深層学習による画像認識技術の生態学への応用-植物種と植生の識別を中心に-. 保全生態学研究, 25: 43-56 9) Li, W., H. Fu, L. Yu, and A. Cracknell. (2016) Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1): 22–35 5) Hoelscher, M-T., Nehls, T., Jänicke, B., and Wessolek, G. (2016) Quantifying cooling effects of facade greening: Shading, transpiration and insulation. Energy and Buildings, 114: 283–290 11) Manso, M., Teot´onio, I., Silva, C.M., and Cruz, C.O. (2021) Green roof and green wall benefits and costs: A review of the quantitative evidence. Renewable and Sustainable Energy Reviews, 135: 110111 14) 野島義照・鈴木弘孝 (2004) 壁面緑化による夏季の壁面から屋内への熱流および熱流量の軽減効果. ランドスケープ研究, 67(5): 447-452 1) Djedjig, R., Belarbi, R., and Bozonnet, E. (2017) Green wall impacts inside and outside buildings: experimental study. Energy Procedia, 139: 578-583 15) Sanz, R., Rosell, J.,R., Llorens, J., Gil, E., Planas, S. (2013) Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D dynamic measurement system. Agricultural Forest Meteorology 171: 153e162. 18) 鈴木弘孝・三坂育正・水谷敦司・田代順孝 (2006) WBGT,SET*による壁面緑化の温熱環境改善効果の評価. ランドスケープ研究, 69(5): 441-446. |
| References_xml | – reference: 18) 鈴木弘孝・三坂育正・水谷敦司・田代順孝 (2006) WBGT,SET*による壁面緑化の温熱環境改善効果の評価. ランドスケープ研究, 69(5): 441-446. – reference: 5) Hoelscher, M-T., Nehls, T., Jänicke, B., and Wessolek, G. (2016) Quantifying cooling effects of facade greening: Shading, transpiration and insulation. Energy and Buildings, 114: 283–290 – reference: 9) Li, W., H. Fu, L. Yu, and A. Cracknell. (2016) Deep learning based oil palm tree detection and counting for high-resolution remote sensing images. Remote Sensing, 9(1): 22–35 – reference: 10) Kong, F.H., Yan, W.J., Zheng, G., Yin, H.W., Cavan, G., Zhan, W.F., Zhang, N. Cheng, L. (2016) Retrieval of three-dimensional tree canopy and shade using terrestrial laser scanning (TLS) data to analyze the cooling effect of vegetation. Agricultural Forest Meteorology, 217: 22e34 – reference: 19) 鈴木弘孝・三坂育正・田代順孝 (2007) 蒸散効率を指標とした壁面緑化の蒸散特性. ランドスケープ研究, 70(5): 401-406 – reference: 7) 加藤 顕・田村 太壱・市橋 新・小林 達明・高橋 輝昌 (2019) 地上レーザーを用いた階層構造と植被率の自動解析手法. 日本緑化工学会誌, 45(1): 121-126 – reference: 1) Djedjig, R., Belarbi, R., and Bozonnet, E. (2017) Green wall impacts inside and outside buildings: experimental study. Energy Procedia, 139: 578-583 – reference: 15) Sanz, R., Rosell, J.,R., Llorens, J., Gil, E., Planas, S. (2013) Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D dynamic measurement system. Agricultural Forest Meteorology 171: 153e162. – reference: 2) Goodfellow, I., Bengio,Y., and Courville, A. (2016) Deep Learning. The MIT Press – reference: 8) Ki, D., and Lee, S. (2021) Analyzing the effects of Green View Index of neighborhood streets on walking time using Google Street View and deep learning. Landscape and Urban Planning, 205: 103920 – reference: 16) Sony Neural Network Console, https://dl.sony.com/ja/ （参照: 2022年2月7日） – reference: 3) 彦坂幸毅 (2016) 植物の光合成・物質生産の測定とモデリング. 共立出版, pp. 30-33 – reference: 12) Moorthy, I., Miller, J.R., Berni, J.A.J., Zarco-Tejada, P., Hu, B., Chen, J. (2011) Field characterization of olive (Oleaeuropaea L.) tree crown architecture using terrestrial laser scanning data, Agricultural Forest. Meteorology. 151: 204e214 – reference: 11) Manso, M., Teot´onio, I., Silva, C.M., and Cruz, C.O. (2021) Green roof and green wall benefits and costs: A review of the quantitative evidence. Renewable and Sustainable Energy Reviews, 135: 110111 – reference: 13) 森本幸裕・小林達明 (2007) 最新 環境緑化工学. 朝倉書店 – reference: 21) Yin, H., Kong F., Middel, A., Dronova, I., Xu, H., James P. (2017) Cooling effect of direct green façades during hot summer days: An observational study in Nanjing, China using TIR and 3DPC data. Building and Environment, 116: 195e206 – reference: 6) 加藤 顕,・安藤 祐樹・吉田 俊也・梶原 康司・本多 嘉明・小林 達明 (2014) 簡易型地上レーザーを用いた毎木調査法. 日本緑化工学会誌, 40(1): 136-141 – reference: 14) 野島義照・鈴木弘孝 (2004) 壁面緑化による夏季の壁面から屋内への熱流および熱流量の軽減効果. ランドスケープ研究, 67(5): 447-452 – reference: 22) 渡部俊太郎・大西信徳・皆川まり・伊勢武史 (2020) 深層学習による画像認識技術の生態学への応用-植物種と植生の識別を中心に-. 保全生態学研究, 25: 43-56 – reference: 4) Hirose, T. (2005) Development of the Monsi-Saeki theory on canopy structure and function. Annals of Botany, 95(3): 483-494 – reference: 20) 竹中優揮・村江行忠・栗木 茂・伊藤 優・市川勇太 (2021) 環境配慮型事務所建築に関する研究（第1報）カーボンマイナスを目指したグリーンオフィス棟の概要. 戸田建設株式会社技術研究報告, 47: 1-4 – reference: 17) 鈴木弘孝・三坂育正・村野直康・田代順孝 (2005) 壁面緑化による建物外部の温熱環境改善効果に関する研究. ランドスケープ研究, 68(2): 503-508 |
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| SubjectTerms | carbon neutral Data acquisition Deep learning energy efficient building Green infrastructure green wall Heat islands Image acquisition Leaf area Leaves Plant structures Plants terrestrial laser Urban areas Urban heat islands |
| Title | Leaf area measurement of green wall plants using deep learning with terrestrial laser |
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