{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T01:10:26Z","timestamp":1736212226538,"version":"3.32.0"},"reference-count":67,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T00:00:00Z","timestamp":1534377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Open Fund of State Key Laboratory of Remote Sensing Science","award":["OFSLRSS201721"]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41371330","41001203"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Project of Excellent Young Scientists of Fujian Province","award":["JA14033"]},{"name":"the Program for New Century Excellent Talents in Fujian province","award":["None"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI, WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI and WAI of the leaf-on and leaf-off forest canopy. The PAI and WAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI, WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level (<4%) by adopting appropriate inversion models.<\/jats:p>","DOI":"10.3390\/rs10081297","type":"journal-article","created":{"date-parts":[[2018,8,16]],"date-time":"2018-08-16T15:39:08Z","timestamp":1534433948000},"page":"1297","source":"Crossref","is-referenced-by-count":13,"title":["Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes"],"prefix":"10.3390","volume":"10","author":[{"given":"Jie","family":"Zou","sequence":"first","affiliation":[{"name":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350116, China"},{"name":"State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China"},{"name":"Key Laboratory of Data Mining and Information Sharing, Ministry of Education, Fuzhou 350116, China"}]},{"given":"Yinguo","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5688-2060","authenticated-orcid":false,"given":"Francesco","family":"Chianucci","sequence":"additional","affiliation":[{"name":"CREA\u2014Research Centre for Agriculture and Environment, via della Navicella 2-4, 00184 Rome, Italy"}]},{"given":"Chunna","family":"Mai","sequence":"additional","affiliation":[{"name":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Weimu","family":"Lin","sequence":"additional","affiliation":[{"name":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Peng","family":"Leng","sequence":"additional","affiliation":[{"name":"Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou 350116, China"}]},{"given":"Shezhou","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Bojie","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Geography, Minjiang University, Fuzhou 350108, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1641\/0006-3568(2001)051[0735:FPAGEC]2.0.CO;2","article-title":"Forest processes and global environmental change: Predicting the effects of individual and multiple stressors: We review the effects of several rapidly changing environmental drivers on ecosystem function, discuss interactions among them, and summarize predicted changes in productivity, carbon storage, and water balance","volume":"51","author":"Aber","year":"2001","journal-title":"BioScience"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.agrformet.2003.08.027","article-title":"Review of methods for in situ leaf area index determination: Part I. 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