Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data Article Swipe
Xinyu Zheng
,
Hao Wang
,
Dong Chen
,
Xiongwei Lou
,
Dasheng Wu
,
Luming Fang
,
Dan Dai
,
Liuchang Xu
,
Xingyu Xue
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/f15081315
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/f15081315
Estimating tree height at the national to regional scale is crucial for assessing forest health and forest carbon storage and understanding forest ecosystem processes. It also aids in formulating forest management and restoration policies to mitigate global climate change. Extensive ground-survey data offer a valuable resource for estimating tree height. In tree height estimation modeling, a few comparative studies have examined the effectiveness of global-based versus local-based models, and the spatial heterogeneity of independent variable parameters remains insufficiently explored. This study utilized ~200,000 ground-survey data points covering the entire provincial region to compare the performance of the global-based Ordinary Least Squares (OLS) and Random Forest (RF) model, as well as local-based Geographically Weighted Regression (GWR) model, for predicting the average tree height of Chinese fir forests in Zhejiang Province China. The results showed that the GWR model outperformed both OLS and RF in terms of predictive accuracy, achieving an R-squared (R2) and adjusted R2 of 0.81 and MAE and RMSE of 0.93 and 1.28, respectively. The performance indicated that the local-based GWR held advantages over global-based models, especially in revealing the spatial non-stationarity of forests. Visualization of parameter estimates across independent variables revealed spatial non-stationarity in their impact effects. In mountainous areas with dense forest coverage, the parameter estimates for average age were notably higher, whereas in forests proximate to urban areas, the parameters were comparatively lower. This study demonstrates the effectiveness of large ground-survey data and GWR in tree height estimation modeling at a provincial scale.
Related Topics
Concepts
Ordinary least squares
Random forest
Forest inventory
Estimation
Forest ecology
Statistics
Tree (set theory)
Forest management
Environmental science
Geography
Regression analysis
Mean squared error
Geographically Weighted Regression
Physical geography
Mathematics
Forestry
Ecosystem
Ecology
Computer science
Management
Mathematical analysis
Machine learning
Biology
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f15081315
- https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977
- OA Status
- gold
- Cited By
- 1
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4401084772
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4401084772Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/f15081315Digital Object Identifier
- Title
-
Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey DataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-28Full publication date if available
- Authors
-
Xinyu Zheng, Hao Wang, Dong Chen, Xiongwei Lou, Dasheng Wu, Luming Fang, Dan Dai, Liuchang Xu, Xingyu XueList of authors in order
- Landing page
-
https://doi.org/10.3390/f15081315Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977Direct OA link when available
- Concepts
-
Ordinary least squares, Random forest, Forest inventory, Estimation, Forest ecology, Statistics, Tree (set theory), Forest management, Environmental science, Geography, Regression analysis, Mean squared error, Geographically Weighted Regression, Physical geography, Mathematics, Forestry, Ecosystem, Ecology, Computer science, Management, Mathematical analysis, Machine learning, Biology, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1Per-year citation counts (last 5 years)
- References (count)
-
46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4401084772 |
|---|---|
| doi | https://doi.org/10.3390/f15081315 |
| ids.doi | https://doi.org/10.3390/f15081315 |
| ids.openalex | https://openalex.org/W4401084772 |
| fwci | 1.34175613 |
| type | article |
| title | Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data |
| biblio.issue | 8 |
| biblio.volume | 15 |
| biblio.last_page | 1315 |
| biblio.first_page | 1315 |
| topics[0].id | https://openalex.org/T11880 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2309 |
| topics[0].subfield.display_name | Nature and Landscape Conservation |
| topics[0].display_name | Forest ecology and management |
| topics[1].id | https://openalex.org/T11164 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9959999918937683 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2305 |
| topics[1].subfield.display_name | Environmental Engineering |
| topics[1].display_name | Remote Sensing and LiDAR Applications |
| topics[2].id | https://openalex.org/T10111 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9933000206947327 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Remote Sensing in Agriculture |
| is_xpac | False |
| apc_list.value | 2000 |
| apc_list.currency | CHF |
| apc_list.value_usd | 2165 |
| apc_paid.value | 2000 |
| apc_paid.currency | CHF |
| apc_paid.value_usd | 2165 |
| concepts[0].id | https://openalex.org/C99656134 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6522260308265686 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2912993 |
| concepts[0].display_name | Ordinary least squares |
| concepts[1].id | https://openalex.org/C169258074 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5612777471542358 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[1].display_name | Random forest |
| concepts[2].id | https://openalex.org/C147103442 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5111327767372131 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1423188 |
| concepts[2].display_name | Forest inventory |
| concepts[3].id | https://openalex.org/C96250715 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5007457733154297 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q965330 |
| concepts[3].display_name | Estimation |
| concepts[4].id | https://openalex.org/C73935091 |
| concepts[4].level | 3 |
| concepts[4].score | 0.46395817399024963 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q2249329 |
| concepts[4].display_name | Forest ecology |
| concepts[5].id | https://openalex.org/C105795698 |
| concepts[5].level | 1 |
| concepts[5].score | 0.46104854345321655 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q12483 |
| concepts[5].display_name | Statistics |
| concepts[6].id | https://openalex.org/C113174947 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4566437900066376 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[6].display_name | Tree (set theory) |
| concepts[7].id | https://openalex.org/C28631016 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4504304528236389 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q372561 |
| concepts[7].display_name | Forest management |
| concepts[8].id | https://openalex.org/C39432304 |
| concepts[8].level | 0 |
| concepts[8].score | 0.445919007062912 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[8].display_name | Environmental science |
| concepts[9].id | https://openalex.org/C205649164 |
| concepts[9].level | 0 |
| concepts[9].score | 0.41930264234542847 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[9].display_name | Geography |
| concepts[10].id | https://openalex.org/C152877465 |
| concepts[10].level | 2 |
| concepts[10].score | 0.41860198974609375 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q208042 |
| concepts[10].display_name | Regression analysis |
| concepts[11].id | https://openalex.org/C139945424 |
| concepts[11].level | 2 |
| concepts[11].score | 0.4141005873680115 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1940696 |
| concepts[11].display_name | Mean squared error |
| concepts[12].id | https://openalex.org/C2910321205 |
| concepts[12].level | 2 |
| concepts[12].score | 0.4107678234577179 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1938983 |
| concepts[12].display_name | Geographically Weighted Regression |
| concepts[13].id | https://openalex.org/C100970517 |
| concepts[13].level | 1 |
| concepts[13].score | 0.34531158208847046 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q52107 |
| concepts[13].display_name | Physical geography |
| concepts[14].id | https://openalex.org/C33923547 |
| concepts[14].level | 0 |
| concepts[14].score | 0.2835848927497864 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[14].display_name | Mathematics |
| concepts[15].id | https://openalex.org/C97137747 |
| concepts[15].level | 1 |
| concepts[15].score | 0.2525908350944519 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q38112 |
| concepts[15].display_name | Forestry |
| concepts[16].id | https://openalex.org/C110872660 |
| concepts[16].level | 2 |
| concepts[16].score | 0.23333406448364258 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q37813 |
| concepts[16].display_name | Ecosystem |
| concepts[17].id | https://openalex.org/C18903297 |
| concepts[17].level | 1 |
| concepts[17].score | 0.23265185952186584 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[17].display_name | Ecology |
| concepts[18].id | https://openalex.org/C41008148 |
| concepts[18].level | 0 |
| concepts[18].score | 0.1850510835647583 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[18].display_name | Computer science |
| concepts[19].id | https://openalex.org/C187736073 |
| concepts[19].level | 1 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q2920921 |
| concepts[19].display_name | Management |
| concepts[20].id | https://openalex.org/C134306372 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[20].display_name | Mathematical analysis |
| concepts[21].id | https://openalex.org/C119857082 |
| concepts[21].level | 1 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[21].display_name | Machine learning |
| concepts[22].id | https://openalex.org/C86803240 |
| concepts[22].level | 0 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[22].display_name | Biology |
| concepts[23].id | https://openalex.org/C162324750 |
| concepts[23].level | 0 |
| concepts[23].score | 0.0 |
| concepts[23].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[23].display_name | Economics |
| keywords[0].id | https://openalex.org/keywords/ordinary-least-squares |
| keywords[0].score | 0.6522260308265686 |
| keywords[0].display_name | Ordinary least squares |
| keywords[1].id | https://openalex.org/keywords/random-forest |
| keywords[1].score | 0.5612777471542358 |
| keywords[1].display_name | Random forest |
| keywords[2].id | https://openalex.org/keywords/forest-inventory |
| keywords[2].score | 0.5111327767372131 |
| keywords[2].display_name | Forest inventory |
| keywords[3].id | https://openalex.org/keywords/estimation |
| keywords[3].score | 0.5007457733154297 |
| keywords[3].display_name | Estimation |
| keywords[4].id | https://openalex.org/keywords/forest-ecology |
| keywords[4].score | 0.46395817399024963 |
| keywords[4].display_name | Forest ecology |
| keywords[5].id | https://openalex.org/keywords/statistics |
| keywords[5].score | 0.46104854345321655 |
| keywords[5].display_name | Statistics |
| keywords[6].id | https://openalex.org/keywords/tree |
| keywords[6].score | 0.4566437900066376 |
| keywords[6].display_name | Tree (set theory) |
| keywords[7].id | https://openalex.org/keywords/forest-management |
| keywords[7].score | 0.4504304528236389 |
| keywords[7].display_name | Forest management |
| keywords[8].id | https://openalex.org/keywords/environmental-science |
| keywords[8].score | 0.445919007062912 |
| keywords[8].display_name | Environmental science |
| keywords[9].id | https://openalex.org/keywords/geography |
| keywords[9].score | 0.41930264234542847 |
| keywords[9].display_name | Geography |
| keywords[10].id | https://openalex.org/keywords/regression-analysis |
| keywords[10].score | 0.41860198974609375 |
| keywords[10].display_name | Regression analysis |
| keywords[11].id | https://openalex.org/keywords/mean-squared-error |
| keywords[11].score | 0.4141005873680115 |
| keywords[11].display_name | Mean squared error |
| keywords[12].id | https://openalex.org/keywords/geographically-weighted-regression |
| keywords[12].score | 0.4107678234577179 |
| keywords[12].display_name | Geographically Weighted Regression |
| keywords[13].id | https://openalex.org/keywords/physical-geography |
| keywords[13].score | 0.34531158208847046 |
| keywords[13].display_name | Physical geography |
| keywords[14].id | https://openalex.org/keywords/mathematics |
| keywords[14].score | 0.2835848927497864 |
| keywords[14].display_name | Mathematics |
| keywords[15].id | https://openalex.org/keywords/forestry |
| keywords[15].score | 0.2525908350944519 |
| keywords[15].display_name | Forestry |
| keywords[16].id | https://openalex.org/keywords/ecosystem |
| keywords[16].score | 0.23333406448364258 |
| keywords[16].display_name | Ecosystem |
| keywords[17].id | https://openalex.org/keywords/ecology |
| keywords[17].score | 0.23265185952186584 |
| keywords[17].display_name | Ecology |
| keywords[18].id | https://openalex.org/keywords/computer-science |
| keywords[18].score | 0.1850510835647583 |
| keywords[18].display_name | Computer science |
| language | en |
| locations[0].id | doi:10.3390/f15081315 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S71324801 |
| locations[0].source.issn | 1999-4907 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 1999-4907 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Forests |
| locations[0].source.host_organization | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Forests |
| locations[0].landing_page_url | https://doi.org/10.3390/f15081315 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5105705894 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Xinyu Zheng |
| authorships[0].countries | CN |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[0].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[0].affiliations[1].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[0].affiliations[2].institution_ids | https://openalex.org/I4210134523 |
| authorships[0].affiliations[2].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[0].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[0].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[0].institutions[0].type | government |
| authorships[0].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[0].institutions[0].country_code | CN |
| authorships[0].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[0].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[0].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[0].institutions[1].type | education |
| authorships[0].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[0].institutions[1].country_code | CN |
| authorships[0].institutions[1].display_name | Zhejiang A & F University |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Xinyu Zheng |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[1].author.id | https://openalex.org/A5113360092 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-5723-0187 |
| authorships[1].author.display_name | Hao Wang |
| authorships[1].affiliations[0].raw_affiliation_string | Qianjiangyuan National Park Management Bureau, Quzhou 324000, China |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Hao Wang |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Qianjiangyuan National Park Management Bureau, Quzhou 324000, China |
| authorships[2].author.id | https://openalex.org/A5013116360 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-8118-3889 |
| authorships[2].author.display_name | Dong Chen |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[2].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[2].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[2].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | Zhejiang A & F University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Chen Dong |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[3].author.id | https://openalex.org/A5016991998 |
| authorships[3].author.orcid | https://orcid.org/0000-0002-4333-5471 |
| authorships[3].author.display_name | Xiongwei Lou |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[3].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[3].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[3].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[3].institutions[0].type | education |
| authorships[3].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | Zhejiang A & F University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Xiongwei Lou |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[4].author.id | https://openalex.org/A5033636430 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6316-303X |
| authorships[4].author.display_name | Dasheng Wu |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[4].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[4].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[4].institutions[0].type | education |
| authorships[4].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[4].institutions[0].country_code | CN |
| authorships[4].institutions[0].display_name | Zhejiang A & F University |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Dasheng Wu |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[5].author.id | https://openalex.org/A5091039605 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-5935-1524 |
| authorships[5].author.display_name | Luming Fang |
| authorships[5].countries | CN |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[5].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[5].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[5].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[5].institutions[0].type | education |
| authorships[5].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[5].institutions[0].country_code | CN |
| authorships[5].institutions[0].display_name | Zhejiang A & F University |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Luming Fang |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[6].author.id | https://openalex.org/A5101614117 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-1287-7569 |
| authorships[6].author.display_name | Dan Dai |
| authorships[6].countries | CN |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[6].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[6].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[6].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[6].institutions[0].country_code | CN |
| authorships[6].institutions[0].display_name | Zhejiang A & F University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Dan Dai |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[7].author.id | https://openalex.org/A5064956525 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-7635-7266 |
| authorships[7].author.display_name | Liuchang Xu |
| authorships[7].countries | CN |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[7].affiliations[0].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[7].institutions[0].id | https://openalex.org/I1284762954 |
| authorships[7].institutions[0].ror | https://ror.org/02vj4rn06 |
| authorships[7].institutions[0].type | education |
| authorships[7].institutions[0].lineage | https://openalex.org/I1284762954 |
| authorships[7].institutions[0].country_code | CN |
| authorships[7].institutions[0].display_name | Zhejiang A & F University |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Liuchang Xu |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[8].author.id | https://openalex.org/A5045504628 |
| authorships[8].author.orcid | https://orcid.org/0000-0002-2011-9602 |
| authorships[8].author.display_name | Xingyu Xue |
| authorships[8].countries | CN |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I4210134523 |
| authorships[8].affiliations[0].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[8].affiliations[1].institution_ids | https://openalex.org/I1284762954 |
| authorships[8].affiliations[1].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[8].affiliations[2].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[8].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[8].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[8].institutions[0].type | government |
| authorships[8].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[8].institutions[0].country_code | CN |
| authorships[8].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[8].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[8].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[8].institutions[1].type | education |
| authorships[8].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[8].institutions[1].country_code | CN |
| authorships[8].institutions[1].display_name | Zhejiang A & F University |
| authorships[8].author_position | last |
| authorships[8].raw_author_name | Xingyu Xue |
| authorships[8].is_corresponding | True |
| authorships[8].raw_affiliation_strings | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China, Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China, Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| has_content.pdf | True |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Tree Height Estimation of Chinese Fir Forests Based on Geographically Weighted Regression and Forest Survey Data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11880 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2309 |
| primary_topic.subfield.display_name | Nature and Landscape Conservation |
| primary_topic.display_name | Forest ecology and management |
| related_works | https://openalex.org/W2093761689, https://openalex.org/W1520214864, https://openalex.org/W2748959688, https://openalex.org/W2995340247, https://openalex.org/W3011225402, https://openalex.org/W2032807833, https://openalex.org/W2883563055, https://openalex.org/W2134006231, https://openalex.org/W2765876372, https://openalex.org/W2948227782 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.3390/f15081315 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S71324801 |
| best_oa_location.source.issn | 1999-4907 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 1999-4907 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Forests |
| best_oa_location.source.host_organization | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Forests |
| best_oa_location.landing_page_url | https://doi.org/10.3390/f15081315 |
| primary_location.id | doi:10.3390/f15081315 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S71324801 |
| primary_location.source.issn | 1999-4907 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 1999-4907 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Forests |
| primary_location.source.host_organization | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_name | Multidisciplinary Digital Publishing Institute |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.mdpi.com/1999-4907/15/8/1315/pdf?version=1722143977 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Forests |
| primary_location.landing_page_url | https://doi.org/10.3390/f15081315 |
| publication_date | 2024-07-28 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2587019393, https://openalex.org/W3013471216, https://openalex.org/W2137902219, https://openalex.org/W2951147814, https://openalex.org/W2156808278, https://openalex.org/W3032247621, https://openalex.org/W4394728017, https://openalex.org/W4221110883, https://openalex.org/W2801578709, https://openalex.org/W6856303741, https://openalex.org/W2171329642, https://openalex.org/W1945623823, https://openalex.org/W4385805748, https://openalex.org/W4366979849, https://openalex.org/W4295845724, https://openalex.org/W6692034908, https://openalex.org/W1949910136, https://openalex.org/W2510634777, https://openalex.org/W2774824407, https://openalex.org/W4383213949, https://openalex.org/W1566532634, https://openalex.org/W4291464844, https://openalex.org/W2616303630, https://openalex.org/W4200035802, https://openalex.org/W2047120335, https://openalex.org/W2998570674, https://openalex.org/W2004872488, https://openalex.org/W2156714016, https://openalex.org/W4377290550, https://openalex.org/W2891746825, https://openalex.org/W4213230060, https://openalex.org/W2248404318, https://openalex.org/W2911964244, https://openalex.org/W3090711245, https://openalex.org/W2479940738, https://openalex.org/W2948613401, https://openalex.org/W4387177364, https://openalex.org/W2089743085, https://openalex.org/W6743112403, https://openalex.org/W3200386646, https://openalex.org/W4211131047, https://openalex.org/W2394513771, https://openalex.org/W2035223309, https://openalex.org/W2747207142, https://openalex.org/W4386793972, https://openalex.org/W2259277873 |
| referenced_works_count | 46 |
| abstract_inverted_index.a | 43, 55, 244 |
| abstract_inverted_index.In | 50, 199 |
| abstract_inverted_index.It | 24 |
| abstract_inverted_index.R2 | 153 |
| abstract_inverted_index.RF | 141 |
| abstract_inverted_index.an | 148 |
| abstract_inverted_index.as | 107, 109 |
| abstract_inverted_index.at | 3, 243 |
| abstract_inverted_index.in | 27, 126, 142, 178, 195, 216, 238 |
| abstract_inverted_index.is | 9 |
| abstract_inverted_index.of | 63, 72, 95, 122, 144, 154, 160, 183, 186, 232 |
| abstract_inverted_index.to | 6, 34, 91, 219 |
| abstract_inverted_index.GWR | 135, 171, 237 |
| abstract_inverted_index.MAE | 157 |
| abstract_inverted_index.OLS | 139 |
| abstract_inverted_index.The | 130, 165 |
| abstract_inverted_index.age | 211 |
| abstract_inverted_index.and | 15, 19, 31, 68, 102, 140, 151, 156, 158, 162, 236 |
| abstract_inverted_index.few | 56 |
| abstract_inverted_index.fir | 124 |
| abstract_inverted_index.for | 11, 46, 116, 209 |
| abstract_inverted_index.the | 4, 61, 69, 87, 93, 96, 118, 134, 169, 180, 206, 222, 230 |
| abstract_inverted_index.(R2) | 150 |
| abstract_inverted_index.(RF) | 105 |
| abstract_inverted_index.0.81 | 155 |
| abstract_inverted_index.0.93 | 161 |
| abstract_inverted_index.RMSE | 159 |
| abstract_inverted_index.This | 79, 227 |
| abstract_inverted_index.aids | 26 |
| abstract_inverted_index.also | 25 |
| abstract_inverted_index.both | 138 |
| abstract_inverted_index.data | 41, 84, 235 |
| abstract_inverted_index.have | 59 |
| abstract_inverted_index.held | 172 |
| abstract_inverted_index.over | 174 |
| abstract_inverted_index.that | 133, 168 |
| abstract_inverted_index.tree | 1, 48, 51, 120, 239 |
| abstract_inverted_index.well | 108 |
| abstract_inverted_index.were | 212, 224 |
| abstract_inverted_index.with | 202 |
| abstract_inverted_index.(GWR) | 114 |
| abstract_inverted_index.(OLS) | 101 |
| abstract_inverted_index.1.28, | 163 |
| abstract_inverted_index.Least | 99 |
| abstract_inverted_index.areas | 201 |
| abstract_inverted_index.dense | 203 |
| abstract_inverted_index.large | 233 |
| abstract_inverted_index.model | 136 |
| abstract_inverted_index.offer | 42 |
| abstract_inverted_index.scale | 8 |
| abstract_inverted_index.study | 80, 228 |
| abstract_inverted_index.terms | 143 |
| abstract_inverted_index.their | 196 |
| abstract_inverted_index.urban | 220 |
| abstract_inverted_index.China. | 129 |
| abstract_inverted_index.Forest | 104 |
| abstract_inverted_index.Random | 103 |
| abstract_inverted_index.across | 189 |
| abstract_inverted_index.areas, | 221 |
| abstract_inverted_index.carbon | 17 |
| abstract_inverted_index.entire | 88 |
| abstract_inverted_index.forest | 13, 16, 21, 29, 204 |
| abstract_inverted_index.global | 36 |
| abstract_inverted_index.health | 14 |
| abstract_inverted_index.height | 2, 52, 121, 240 |
| abstract_inverted_index.impact | 197 |
| abstract_inverted_index.lower. | 226 |
| abstract_inverted_index.model, | 106, 115 |
| abstract_inverted_index.points | 85 |
| abstract_inverted_index.region | 90 |
| abstract_inverted_index.scale. | 246 |
| abstract_inverted_index.showed | 132 |
| abstract_inverted_index.versus | 65 |
| abstract_inverted_index.Chinese | 123 |
| abstract_inverted_index.Squares | 100 |
| abstract_inverted_index.average | 119, 210 |
| abstract_inverted_index.change. | 38 |
| abstract_inverted_index.climate | 37 |
| abstract_inverted_index.compare | 92 |
| abstract_inverted_index.crucial | 10 |
| abstract_inverted_index.forests | 125, 217 |
| abstract_inverted_index.height. | 49 |
| abstract_inverted_index.higher, | 214 |
| abstract_inverted_index.models, | 67, 176 |
| abstract_inverted_index.notably | 213 |
| abstract_inverted_index.remains | 76 |
| abstract_inverted_index.results | 131 |
| abstract_inverted_index.spatial | 70, 181, 193 |
| abstract_inverted_index.storage | 18 |
| abstract_inverted_index.studies | 58 |
| abstract_inverted_index.whereas | 215 |
| abstract_inverted_index.Ordinary | 98 |
| abstract_inverted_index.Province | 128 |
| abstract_inverted_index.Weighted | 112 |
| abstract_inverted_index.Zhejiang | 127 |
| abstract_inverted_index.adjusted | 152 |
| abstract_inverted_index.covering | 86 |
| abstract_inverted_index.effects. | 198 |
| abstract_inverted_index.examined | 60 |
| abstract_inverted_index.forests. | 184 |
| abstract_inverted_index.mitigate | 35 |
| abstract_inverted_index.modeling | 242 |
| abstract_inverted_index.national | 5 |
| abstract_inverted_index.policies | 33 |
| abstract_inverted_index.regional | 7 |
| abstract_inverted_index.resource | 45 |
| abstract_inverted_index.revealed | 192 |
| abstract_inverted_index.utilized | 81 |
| abstract_inverted_index.valuable | 44 |
| abstract_inverted_index.variable | 74 |
| abstract_inverted_index.~200,000 | 82 |
| abstract_inverted_index.Extensive | 39 |
| abstract_inverted_index.R-squared | 149 |
| abstract_inverted_index.accuracy, | 146 |
| abstract_inverted_index.achieving | 147 |
| abstract_inverted_index.assessing | 12 |
| abstract_inverted_index.coverage, | 205 |
| abstract_inverted_index.ecosystem | 22 |
| abstract_inverted_index.estimates | 188, 208 |
| abstract_inverted_index.explored. | 78 |
| abstract_inverted_index.indicated | 167 |
| abstract_inverted_index.modeling, | 54 |
| abstract_inverted_index.parameter | 187, 207 |
| abstract_inverted_index.proximate | 218 |
| abstract_inverted_index.revealing | 179 |
| abstract_inverted_index.variables | 191 |
| abstract_inverted_index.Estimating | 0 |
| abstract_inverted_index.Regression | 113 |
| abstract_inverted_index.advantages | 173 |
| abstract_inverted_index.especially | 177 |
| abstract_inverted_index.estimating | 47 |
| abstract_inverted_index.estimation | 53, 241 |
| abstract_inverted_index.management | 30 |
| abstract_inverted_index.parameters | 75, 223 |
| abstract_inverted_index.predicting | 117 |
| abstract_inverted_index.predictive | 145 |
| abstract_inverted_index.processes. | 23 |
| abstract_inverted_index.provincial | 89, 245 |
| abstract_inverted_index.comparative | 57 |
| abstract_inverted_index.formulating | 28 |
| abstract_inverted_index.independent | 73, 190 |
| abstract_inverted_index.local-based | 66, 110, 170 |
| abstract_inverted_index.mountainous | 200 |
| abstract_inverted_index.performance | 94, 166 |
| abstract_inverted_index.restoration | 32 |
| abstract_inverted_index.demonstrates | 229 |
| abstract_inverted_index.global-based | 64, 97, 175 |
| abstract_inverted_index.outperformed | 137 |
| abstract_inverted_index.Visualization | 185 |
| abstract_inverted_index.comparatively | 225 |
| abstract_inverted_index.effectiveness | 62, 231 |
| abstract_inverted_index.ground-survey | 40, 83, 234 |
| abstract_inverted_index.heterogeneity | 71 |
| abstract_inverted_index.respectively. | 164 |
| abstract_inverted_index.understanding | 20 |
| abstract_inverted_index.Geographically | 111 |
| abstract_inverted_index.insufficiently | 77 |
| abstract_inverted_index.non-stationarity | 182, 194 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5045504628 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I1284762954, https://openalex.org/I4210134523 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.49000000953674316 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.75524476 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |