Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine Learning Article Swipe
Ning Fang
,
Linyan Yao
,
Dasheng Wu
,
Xinyu Zheng
,
Shimei Luo
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/f14081630
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/f14081630
Forest ecological function is one of the key indicators reflecting the quality of forest resources. The traditional weighting method to assess forest ecological function is based on a large amount of ground survey data; it is accurate but costly and time-consuming. This study utilized three machine learning algorithms to estimate forest ecological function levels based on multi-source data, including Sentinel-2 optical remote sensing images and digital elevation model (DEM) and forest resource planning and design survey data. The experimental results showed that Random Forest (RF) was the optimal model, with overall accuracy of 0.82, recall of 0.66, and F1 of 0.62, followed by CatBoost (overall accuracy = 0.82, recall = 0.62, F1 = 0.58) and LightGBM (overall accuracy = 0.76, recall = 0.61, F1 = 0.58). Except for the indicators from remote sensing images and DEM data, the five ground survey indicators of forest origin (QI_YUAN), tree age group (LING_ZU), forest category (LIN_ZHONG), dominant species (YOU_SHI_SZ), and tree age (NL) were used in the modeling and prediction. Compared to the traditional methods, the proposed algorithm has lower cost and stronger timeliness.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/f14081630
- https://www.mdpi.com/1999-4907/14/8/1630/pdf?version=1691985335
- OA Status
- gold
- Cited By
- 13
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385805748
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385805748Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/f14081630Digital Object Identifier
- Title
-
Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-12Full publication date if available
- Authors
-
Ning Fang, Linyan Yao, Dasheng Wu, Xinyu Zheng, Shimei LuoList of authors in order
- Landing page
-
https://doi.org/10.3390/f14081630Publisher landing page
- PDF URL
-
https://www.mdpi.com/1999-4907/14/8/1630/pdf?version=1691985335Direct 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/1999-4907/14/8/1630/pdf?version=1691985335Direct OA link when available
- Concepts
-
Random forest, Weighting, Computer science, Remote sensing, Function (biology), Decision tree, Tree (set theory), Recall, Machine learning, Ecology, Environmental science, Geography, Mathematics, Biology, Mathematical analysis, Evolutionary biology, Philosophy, Linguistics, Radiology, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
13Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 10, 2023: 1Per-year citation counts (last 5 years)
- References (count)
-
38Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385805748 |
|---|---|
| doi | https://doi.org/10.3390/f14081630 |
| ids.doi | https://doi.org/10.3390/f14081630 |
| ids.openalex | https://openalex.org/W4385805748 |
| fwci | 2.13046635 |
| type | article |
| title | Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine Learning |
| biblio.issue | 8 |
| biblio.volume | 14 |
| biblio.last_page | 1630 |
| biblio.first_page | 1630 |
| topics[0].id | https://openalex.org/T11164 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9997000098228455 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2305 |
| topics[0].subfield.display_name | Environmental Engineering |
| topics[0].display_name | Remote Sensing and LiDAR Applications |
| topics[1].id | https://openalex.org/T10111 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9994000196456909 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2303 |
| topics[1].subfield.display_name | Ecology |
| topics[1].display_name | Remote Sensing in Agriculture |
| topics[2].id | https://openalex.org/T10226 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9929999709129333 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2306 |
| topics[2].subfield.display_name | Global and Planetary Change |
| topics[2].display_name | Land Use and Ecosystem Services |
| 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/C169258074 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7867260575294495 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q245748 |
| concepts[0].display_name | Random forest |
| concepts[1].id | https://openalex.org/C183115368 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5486463308334351 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q856577 |
| concepts[1].display_name | Weighting |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.48947107791900635 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C62649853 |
| concepts[3].level | 1 |
| concepts[3].score | 0.4698200523853302 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q199687 |
| concepts[3].display_name | Remote sensing |
| concepts[4].id | https://openalex.org/C14036430 |
| concepts[4].level | 2 |
| concepts[4].score | 0.45726311206817627 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q3736076 |
| concepts[4].display_name | Function (biology) |
| concepts[5].id | https://openalex.org/C84525736 |
| concepts[5].level | 2 |
| concepts[5].score | 0.44670355319976807 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q831366 |
| concepts[5].display_name | Decision tree |
| concepts[6].id | https://openalex.org/C113174947 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4363337457180023 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q2859736 |
| concepts[6].display_name | Tree (set theory) |
| concepts[7].id | https://openalex.org/C100660578 |
| concepts[7].level | 2 |
| concepts[7].score | 0.42660313844680786 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q18733 |
| concepts[7].display_name | Recall |
| concepts[8].id | https://openalex.org/C119857082 |
| concepts[8].level | 1 |
| concepts[8].score | 0.39054030179977417 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[8].display_name | Machine learning |
| concepts[9].id | https://openalex.org/C18903297 |
| concepts[9].level | 1 |
| concepts[9].score | 0.3483309745788574 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[9].display_name | Ecology |
| concepts[10].id | https://openalex.org/C39432304 |
| concepts[10].level | 0 |
| concepts[10].score | 0.3322322964668274 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[10].display_name | Environmental science |
| concepts[11].id | https://openalex.org/C205649164 |
| concepts[11].level | 0 |
| concepts[11].score | 0.2699311077594757 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[11].display_name | Geography |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.19717365503311157 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C86803240 |
| concepts[13].level | 0 |
| concepts[13].score | 0.07508385181427002 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[13].display_name | Biology |
| concepts[14].id | https://openalex.org/C134306372 |
| concepts[14].level | 1 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q7754 |
| concepts[14].display_name | Mathematical analysis |
| concepts[15].id | https://openalex.org/C78458016 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q840400 |
| concepts[15].display_name | Evolutionary biology |
| concepts[16].id | https://openalex.org/C138885662 |
| concepts[16].level | 0 |
| concepts[16].score | 0.0 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[16].display_name | Philosophy |
| concepts[17].id | https://openalex.org/C41895202 |
| concepts[17].level | 1 |
| concepts[17].score | 0.0 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q8162 |
| concepts[17].display_name | Linguistics |
| concepts[18].id | https://openalex.org/C126838900 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[18].display_name | Radiology |
| concepts[19].id | https://openalex.org/C71924100 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[19].display_name | Medicine |
| keywords[0].id | https://openalex.org/keywords/random-forest |
| keywords[0].score | 0.7867260575294495 |
| keywords[0].display_name | Random forest |
| keywords[1].id | https://openalex.org/keywords/weighting |
| keywords[1].score | 0.5486463308334351 |
| keywords[1].display_name | Weighting |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.48947107791900635 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/remote-sensing |
| keywords[3].score | 0.4698200523853302 |
| keywords[3].display_name | Remote sensing |
| keywords[4].id | https://openalex.org/keywords/function |
| keywords[4].score | 0.45726311206817627 |
| keywords[4].display_name | Function (biology) |
| keywords[5].id | https://openalex.org/keywords/decision-tree |
| keywords[5].score | 0.44670355319976807 |
| keywords[5].display_name | Decision tree |
| keywords[6].id | https://openalex.org/keywords/tree |
| keywords[6].score | 0.4363337457180023 |
| keywords[6].display_name | Tree (set theory) |
| keywords[7].id | https://openalex.org/keywords/recall |
| keywords[7].score | 0.42660313844680786 |
| keywords[7].display_name | Recall |
| keywords[8].id | https://openalex.org/keywords/machine-learning |
| keywords[8].score | 0.39054030179977417 |
| keywords[8].display_name | Machine learning |
| keywords[9].id | https://openalex.org/keywords/ecology |
| keywords[9].score | 0.3483309745788574 |
| keywords[9].display_name | Ecology |
| keywords[10].id | https://openalex.org/keywords/environmental-science |
| keywords[10].score | 0.3322322964668274 |
| keywords[10].display_name | Environmental science |
| keywords[11].id | https://openalex.org/keywords/geography |
| keywords[11].score | 0.2699311077594757 |
| keywords[11].display_name | Geography |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.19717365503311157 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/biology |
| keywords[13].score | 0.07508385181427002 |
| keywords[13].display_name | Biology |
| language | en |
| locations[0].id | doi:10.3390/f14081630 |
| 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/1999-4907/14/8/1630/pdf?version=1691985335 |
| 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/f14081630 |
| locations[1].id | pmh:oai:mdpi.com:/1999-4907/14/8/1630/ |
| locations[1].is_oa | True |
| locations[1].source.id | https://openalex.org/S4306400947 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | True |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | MDPI (MDPI AG) |
| locations[1].source.host_organization | https://openalex.org/I4210097602 |
| locations[1].source.host_organization_name | Multidisciplinary Digital Publishing Institute (Switzerland) |
| locations[1].source.host_organization_lineage | https://openalex.org/I4210097602 |
| locations[1].license | cc-by |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | Text |
| locations[1].license_id | https://openalex.org/licenses/cc-by |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Forests; Volume 14; Issue 8; Pages: 1630 |
| locations[1].landing_page_url | https://dx.doi.org/10.3390/f14081630 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5052064475 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-9380-3608 |
| authorships[0].author.display_name | Ning Fang |
| 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].institution_ids | https://openalex.org/I4210134523 |
| authorships[0].affiliations[1].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[0].affiliations[2].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, 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 | Ning Fang |
| 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/A5101138591 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Linyan Yao |
| authorships[1].affiliations[0].raw_affiliation_string | Zhejiang Public Information Industry Co., Ltd., Hangzhou 310000, China |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Linyan Yao |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Zhejiang Public Information Industry Co., Ltd., Hangzhou 310000, China |
| authorships[2].author.id | https://openalex.org/A5033636430 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-6316-303X |
| authorships[2].author.display_name | Dasheng Wu |
| authorships[2].countries | CN |
| authorships[2].affiliations[0].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[2].affiliations[1].institution_ids | https://openalex.org/I1284762954 |
| authorships[2].affiliations[1].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[2].affiliations[2].institution_ids | https://openalex.org/I4210134523 |
| authorships[2].affiliations[2].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[2].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[2].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[2].institutions[0].type | government |
| authorships[2].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[2].institutions[0].country_code | CN |
| authorships[2].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[2].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[2].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[2].institutions[1].type | education |
| authorships[2].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[2].institutions[1].country_code | CN |
| authorships[2].institutions[1].display_name | Zhejiang A & F University |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Dasheng Wu |
| authorships[2].is_corresponding | False |
| authorships[2].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[3].author.id | https://openalex.org/A5082482226 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-1840-214X |
| authorships[3].author.display_name | Xinyu Zheng |
| authorships[3].countries | CN |
| authorships[3].affiliations[0].raw_affiliation_string | Key Laboratory of Forestry Intelligent Monitoring and Information Technology of Zhejiang Province, Hangzhou 311300, China |
| authorships[3].affiliations[1].institution_ids | https://openalex.org/I4210134523 |
| authorships[3].affiliations[1].raw_affiliation_string | Key Laboratory of State Forestry and Grassland Administration on Forestry Sensing Technology and Intelligent Equipment, Hangzhou 311300, China |
| authorships[3].affiliations[2].institution_ids | https://openalex.org/I1284762954 |
| authorships[3].affiliations[2].raw_affiliation_string | College of Mathematics and Computer Science, Zhejiang A&F University, Hangzhou 311300, China |
| authorships[3].institutions[0].id | https://openalex.org/I4210134523 |
| authorships[3].institutions[0].ror | https://ror.org/03f2n3n81 |
| authorships[3].institutions[0].type | government |
| authorships[3].institutions[0].lineage | https://openalex.org/I4210134523 |
| authorships[3].institutions[0].country_code | CN |
| authorships[3].institutions[0].display_name | State Forestry and Grassland Administration |
| authorships[3].institutions[1].id | https://openalex.org/I1284762954 |
| authorships[3].institutions[1].ror | https://ror.org/02vj4rn06 |
| authorships[3].institutions[1].type | education |
| authorships[3].institutions[1].lineage | https://openalex.org/I1284762954 |
| authorships[3].institutions[1].country_code | CN |
| authorships[3].institutions[1].display_name | Zhejiang A & F University |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Xinyu Zheng |
| authorships[3].is_corresponding | False |
| authorships[3].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[4].author.id | https://openalex.org/A5113143475 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Shimei Luo |
| authorships[4].countries | CN |
| authorships[4].affiliations[0].institution_ids | https://openalex.org/I1284762954 |
| authorships[4].affiliations[0].raw_affiliation_string | College of Economic and Management, 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 | last |
| authorships[4].raw_author_name | Shimei Luo |
| authorships[4].is_corresponding | True |
| authorships[4].raw_affiliation_strings | College of Economic and Management, Zhejiang A&F University, Hangzhou 311300, China |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.mdpi.com/1999-4907/14/8/1630/pdf?version=1691985335 |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2023-08-15T00:00:00 |
| display_name | Assessment of Forest Ecological Function Levels Based on Multi-Source Data and Machine Learning |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11164 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9997000098228455 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2305 |
| primary_topic.subfield.display_name | Environmental Engineering |
| primary_topic.display_name | Remote Sensing and LiDAR Applications |
| related_works | https://openalex.org/W2180954594, https://openalex.org/W2052835778, https://openalex.org/W2049003611, https://openalex.org/W2127804977, https://openalex.org/W2108418243, https://openalex.org/W4366990902, https://openalex.org/W4317732970, https://openalex.org/W4388550696, https://openalex.org/W4313289487, https://openalex.org/W4321636153 |
| cited_by_count | 13 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| counts_by_year[1].year | 2024 |
| counts_by_year[1].cited_by_count | 10 |
| counts_by_year[2].year | 2023 |
| counts_by_year[2].cited_by_count | 1 |
| locations_count | 2 |
| best_oa_location.id | doi:10.3390/f14081630 |
| 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/1999-4907/14/8/1630/pdf?version=1691985335 |
| 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/f14081630 |
| primary_location.id | doi:10.3390/f14081630 |
| 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/1999-4907/14/8/1630/pdf?version=1691985335 |
| 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/f14081630 |
| publication_date | 2023-08-12 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W4280635600, https://openalex.org/W2911937602, https://openalex.org/W1977175590, https://openalex.org/W2120841153, https://openalex.org/W2895261078, https://openalex.org/W2470393719, https://openalex.org/W4224268454, https://openalex.org/W2067765114, https://openalex.org/W2884119812, https://openalex.org/W1973618291, https://openalex.org/W4322742038, https://openalex.org/W2463155406, https://openalex.org/W6681364406, https://openalex.org/W2806984957, https://openalex.org/W4224316298, https://openalex.org/W4313581329, https://openalex.org/W3142909427, https://openalex.org/W3090711245, https://openalex.org/W4361199567, https://openalex.org/W2018732570, https://openalex.org/W4317434663, https://openalex.org/W4319317590, https://openalex.org/W4313584649, https://openalex.org/W4307703616, https://openalex.org/W6750594068, https://openalex.org/W2107260781, https://openalex.org/W3094508301, https://openalex.org/W2482464033, https://openalex.org/W2040871222, https://openalex.org/W3173753471, https://openalex.org/W4244713694, https://openalex.org/W4303980722, https://openalex.org/W2517711968, https://openalex.org/W3183852519, https://openalex.org/W3082077183, https://openalex.org/W3000409346, https://openalex.org/W4242879511, https://openalex.org/W2799964016 |
| referenced_works_count | 38 |
| abstract_inverted_index.= | 106, 109, 112, 118, 121, 124 |
| abstract_inverted_index.a | 27 |
| abstract_inverted_index.F1 | 98, 111, 123 |
| abstract_inverted_index.by | 102 |
| abstract_inverted_index.in | 162 |
| abstract_inverted_index.is | 3, 24, 35 |
| abstract_inverted_index.it | 34 |
| abstract_inverted_index.of | 5, 12, 30, 92, 95, 99, 142 |
| abstract_inverted_index.on | 26, 55 |
| abstract_inverted_index.to | 19, 48, 168 |
| abstract_inverted_index.DEM | 135 |
| abstract_inverted_index.The | 15, 77 |
| abstract_inverted_index.age | 147, 158 |
| abstract_inverted_index.and | 39, 64, 69, 73, 97, 114, 134, 156, 165, 178 |
| abstract_inverted_index.but | 37 |
| abstract_inverted_index.for | 127 |
| abstract_inverted_index.has | 175 |
| abstract_inverted_index.key | 7 |
| abstract_inverted_index.one | 4 |
| abstract_inverted_index.the | 6, 10, 86, 128, 137, 163, 169, 172 |
| abstract_inverted_index.was | 85 |
| abstract_inverted_index.(NL) | 159 |
| abstract_inverted_index.(RF) | 84 |
| abstract_inverted_index.This | 41 |
| abstract_inverted_index.cost | 177 |
| abstract_inverted_index.five | 138 |
| abstract_inverted_index.from | 130 |
| abstract_inverted_index.that | 81 |
| abstract_inverted_index.tree | 146, 157 |
| abstract_inverted_index.used | 161 |
| abstract_inverted_index.were | 160 |
| abstract_inverted_index.with | 89 |
| abstract_inverted_index.(DEM) | 68 |
| abstract_inverted_index.0.58) | 113 |
| abstract_inverted_index.0.61, | 122 |
| abstract_inverted_index.0.62, | 100, 110 |
| abstract_inverted_index.0.66, | 96 |
| abstract_inverted_index.0.76, | 119 |
| abstract_inverted_index.0.82, | 93, 107 |
| abstract_inverted_index.based | 25, 54 |
| abstract_inverted_index.data, | 57, 136 |
| abstract_inverted_index.data. | 76 |
| abstract_inverted_index.data; | 33 |
| abstract_inverted_index.group | 148 |
| abstract_inverted_index.large | 28 |
| abstract_inverted_index.lower | 176 |
| abstract_inverted_index.model | 67 |
| abstract_inverted_index.study | 42 |
| abstract_inverted_index.three | 44 |
| abstract_inverted_index.0.58). | 125 |
| abstract_inverted_index.Except | 126 |
| abstract_inverted_index.Forest | 0, 83 |
| abstract_inverted_index.Random | 82 |
| abstract_inverted_index.amount | 29 |
| abstract_inverted_index.assess | 20 |
| abstract_inverted_index.costly | 38 |
| abstract_inverted_index.design | 74 |
| abstract_inverted_index.forest | 13, 21, 50, 70, 143, 150 |
| abstract_inverted_index.ground | 31, 139 |
| abstract_inverted_index.images | 63, 133 |
| abstract_inverted_index.levels | 53 |
| abstract_inverted_index.method | 18 |
| abstract_inverted_index.model, | 88 |
| abstract_inverted_index.origin | 144 |
| abstract_inverted_index.recall | 94, 108, 120 |
| abstract_inverted_index.remote | 61, 131 |
| abstract_inverted_index.showed | 80 |
| abstract_inverted_index.survey | 32, 75, 140 |
| abstract_inverted_index.digital | 65 |
| abstract_inverted_index.machine | 45 |
| abstract_inverted_index.optical | 60 |
| abstract_inverted_index.optimal | 87 |
| abstract_inverted_index.overall | 90 |
| abstract_inverted_index.quality | 11 |
| abstract_inverted_index.results | 79 |
| abstract_inverted_index.sensing | 62, 132 |
| abstract_inverted_index.species | 154 |
| abstract_inverted_index.(overall | 104, 116 |
| abstract_inverted_index.CatBoost | 103 |
| abstract_inverted_index.Compared | 167 |
| abstract_inverted_index.LightGBM | 115 |
| abstract_inverted_index.accuracy | 91, 105, 117 |
| abstract_inverted_index.accurate | 36 |
| abstract_inverted_index.category | 151 |
| abstract_inverted_index.dominant | 153 |
| abstract_inverted_index.estimate | 49 |
| abstract_inverted_index.followed | 101 |
| abstract_inverted_index.function | 2, 23, 52 |
| abstract_inverted_index.learning | 46 |
| abstract_inverted_index.methods, | 171 |
| abstract_inverted_index.modeling | 164 |
| abstract_inverted_index.planning | 72 |
| abstract_inverted_index.proposed | 173 |
| abstract_inverted_index.resource | 71 |
| abstract_inverted_index.stronger | 179 |
| abstract_inverted_index.utilized | 43 |
| abstract_inverted_index.algorithm | 174 |
| abstract_inverted_index.elevation | 66 |
| abstract_inverted_index.including | 58 |
| abstract_inverted_index.weighting | 17 |
| abstract_inverted_index.(LING_ZU), | 149 |
| abstract_inverted_index.(QI_YUAN), | 145 |
| abstract_inverted_index.Sentinel-2 | 59 |
| abstract_inverted_index.algorithms | 47 |
| abstract_inverted_index.ecological | 1, 22, 51 |
| abstract_inverted_index.indicators | 8, 129, 141 |
| abstract_inverted_index.reflecting | 9 |
| abstract_inverted_index.resources. | 14 |
| abstract_inverted_index.prediction. | 166 |
| abstract_inverted_index.timeliness. | 180 |
| abstract_inverted_index.traditional | 16, 170 |
| abstract_inverted_index.(LIN_ZHONG), | 152 |
| abstract_inverted_index.experimental | 78 |
| abstract_inverted_index.multi-source | 56 |
| abstract_inverted_index.(YOU_SHI_SZ), | 155 |
| abstract_inverted_index.time-consuming. | 40 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5113143475 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I1284762954 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.6600000262260437 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.84293194 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |