Search
| Leader | 00000nz a2200037n 45 0 | ||
|---|---|---|---|
| 001 | WKP|Q92050606 (VIAF cluster) (Authority/Source Record) | ||
| 003 | WKP | ||
| 005 | 20241120235942.0 | ||
| 008 | 241120nneanz||abbn n and d | ||
| 035 | ‡a (WKP)Q92050606 | ||
| 024 | ‡a 0000-0001-9281-4260 ‡2 orcid | ||
| 035 | ‡a (OCoLC)Q92050606 | ||
| 100 | 0 | ‡a Zhen Zhen ‡c researcher (ORCID 0000-0001-9281-4260) ‡9 en | |
| 400 | 0 | ‡a Zhen Zhen ‡c wetenschapper ‡9 nl | |
| 670 | ‡a Author's An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China | ||
| 670 | ‡a Author's Forest carbon sink in North China increased in recent two decades, but decreased in extreme drought years | ||
| 670 | ‡a Author's Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018 | ||
| 670 | ‡a Author's Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach | ||
| 670 | ‡a Author's Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests | ||
| 670 | ‡a Author's Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018 | ||
| 670 | ‡a Author's The Effect of Synergistic Approaches of Features and Ensemble Learning Algorith on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8 | ||
| 909 | ‡a (orcid) 0000000192814260 ‡9 1 | ||
| 919 | ‡a spatiotemporalheterogeneityandthekeyinfluencingfactorsofpm25andpm10inheilongjiangchinafrom2014to ‡A Spatiotemporal Heterogeneity and the Key Influencing Factors of PM2.5 and PM10 in Heilongjiang, China from 2014 to 2018 ‡9 1 | ||
| 919 | ‡a novelfeaturesofcanopyheightdistributionforabovegroundbiomassestimationusingmachinelearningacasestudyinnaturalsecondaryforests ‡A Novel Features of Canopy Height Distribution for Aboveground Biomass Estimation Using Machine Learning: A Case Study in Natural Secondary Forests ‡9 1 | ||
| 919 | ‡a multiplatformlidarfornondestructiveindividualabovegroundbiomassestimationforchangbailarchlarixolgensishenryusingahierarchicalbayesianapproach ‡A Multi-Platform LiDAR for Non-Destructive Individual Aboveground Biomass Estimation for Changbai Larch (Larix olgensis Henry) Using a Hierarchical Bayesian Approach ‡9 1 | ||
| 919 | ‡a globalandgeographicallyandtemporallyweightedregressionmodelsformodelingpm25inheilongjiangchinafrom2015to ‡A Global and Geographically and Temporally Weighted Regression Models for Modeling PM2.5 in Heilongjiang, China from 2015 to 2018 ‡9 1 | ||
| 919 | ‡a innovativelightweight1dcnnmodelforefficientmonitoringoflargescaleforestcompositionacasestudyofheilongjiangprovincechina ‡A An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China ‡9 1 | ||
| 919 | ‡a effectofsynergisticapproachesoffeaturesandensemblelearningalgorithonabovegroundbiomassestimationofnaturalsecondaryforestsbasedonalsandlandsat8 ‡A The Effect of Synergistic Approaches of Features and Ensemble Learning Algorith on Aboveground Biomass Estimation of Natural Secondary Forests Based on ALS and Landsat 8 ‡9 1 | ||
| 919 | ‡a forestcarbonsinkinnorthchinaincreasedinrecent2decadesbutdecreasedinextremedroughtyears ‡A Forest carbon sink in North China increased in recent two decades, but decreased in extreme drought years ‡9 1 | ||
| 943 | ‡a 201x ‡A 2018 ‡9 2 | ||
| 996 | ‡2 J9U|987007299947005171 | ||
| 996 | ‡2 CAOONL|ncf11573028 | ||
| 996 | ‡2 PLWABN|9810686184105606 | ||
| 996 | ‡2 DNB|121385027 | ||
| 996 | ‡2 DNB|121773788X | ||
| 996 | ‡2 ISNI|0000000067744179 | ||
| 996 | ‡2 CYT|AC000519119 | ||
| 996 | ‡2 DNB|1136732683 | ||
| 996 | ‡2 SUDOC|198229054 | ||
| 996 | ‡2 DNB|1326399128 | ||
| 996 | ‡2 ISNI|000000008892868X | ||
| 996 | ‡2 SUDOC|067009573 | ||
| 996 | ‡2 DNB|1307194699 | ||
| 996 | ‡2 PLWABN|9813658375405606 | ||
| 996 | ‡2 ISNI|0000000063326703 | ||
| 996 | ‡2 LC|n 2003063815 | ||
| 996 | ‡2 LC|n 99048930 | ||
| 996 | ‡2 B2Q|0000385884 | ||
| 996 | ‡2 BIBSYS|10103418 | ||
| 996 | ‡2 DNB|130633652X | ||
| 996 | ‡2 NII|DA04163390 | ||
| 996 | ‡2 DNB|1302354132 | ||
| 996 | ‡2 DNB|103794626X | ||
| 996 | ‡2 NII|DA14301774 | ||
| 996 | ‡2 DNB|1016240570 | ||
| 996 | ‡2 LC|no2019124659 | ||
| 996 | ‡2 DNB|1307195490 | ||
| 996 | ‡2 NSK|000247558 | ||
| 996 | ‡2 CYT|AC000653587 | ||
| 996 | ‡2 DNB|133796809 | ||
| 996 | ‡2 DNB|1192714377 | ||
| 996 | ‡2 RERO|A006517910 | ||
| 996 | ‡2 DNB|1234066025 | ||
| 996 | ‡2 NDL|00688553 | ||
| 996 | ‡2 DNB|139299386 | ||
| 996 | ‡2 DNB|1337340448 | ||
| 996 | ‡2 ISNI|0000000108504383 | ||
| 996 | ‡2 LNB|LNC10-000191222 | ||
| 996 | ‡2 LC|n 79020821 | ||
| 996 | ‡2 SUDOC|180839756 | ||
| 996 | ‡2 DNB|1072186500 | ||
| 996 | ‡2 DNB|1060130033 | ||
| 996 | ‡2 ICCU|TO0V475241 | ||
| 996 | ‡2 DNB|1273118944 | ||
| 996 | ‡2 BNF|16591477 | ||
| 996 | ‡2 DNB|1192354974 | ||
| 996 | ‡2 DNB|1136616101 | ||
| 996 | ‡2 ISNI|0000000373037026 | ||
| 996 | ‡2 DNB|1021718068 | ||
| 996 | ‡2 RERO|A008843073 | ||
| 996 | ‡2 BNC|981058518316306706 | ||
| 996 | ‡2 BNF|12249624 | ||
| 996 | ‡2 DNB|1026391393 | ||
| 996 | ‡2 LC|no2020125683 | ||
| 996 | ‡2 NSK|000451436 | ||
| 996 | ‡2 NSK|000280749 | ||
| 996 | ‡2 BNF|17820032 | ||
| 996 | ‡2 NDL|01204351 | ||
| 996 | ‡2 BIBSYS|10070086 | ||
| 996 | ‡2 NII|DA03364422 | ||
| 996 | ‡2 DNB|1345199570 | ||
| 996 | ‡2 ISNI|0000000119447505 | ||
| 996 | ‡2 DNB|138780692 | ||
| 996 | ‡2 CAOONL|ncf10426538 | ||
| 996 | ‡2 JPG|500116214 | ||
| 996 | ‡2 DNB|1207305170 | ||
| 996 | ‡2 LC|no2010197820 | ||
| 997 | ‡a 0 0 lived 0 0 ‡9 1 |
