[1] 汤旭光, 刘殿伟, 王宗明, 等. 森林地上生物量遥感估算研究进展[J]. 生态学杂志, 2012, 31(5): 1311−1318.

TANG Xuguang, LIU Dianwei, WANG Zongming, et al. Estimation of forest aboveground biomass based on remote sensing data: a review [J]. Chinese Journal of Ecology, 2012, 31(5): 1311−1318.
[2] 张少伟, 惠刚盈, 韩宗涛, 等. 基于光学多光谱与SAR遥感特征快速优化的大区域森林地上生物量估测[J]. 遥感技术与应用, 2019, 34(5): 925−938.

ZHANG Shaowei, HUI Gangying, HAN Zongtao, et al. Estimation of large-scale forest above-ground biomass based on fast optimizing remotely sensed features from pptical multi-spectral and SAR data [J]. Remote Sensing Technology and Application, 2019, 34(5): 925−938.
[3] 谢福明, 字李, 舒清态. 基于优化k-NN模型的高山松地上生物量遥感估测[J]. 浙江农林大学学报, 2019, 36(3): 515−523.

XIE Fuming, ZI Li, SHU Qingtai. Optimizing the k-nearest neighbors technique for estimating Pinus densata aboveground biomass based on remote sensing [J]. Journal of Zhejiang A&F University, 2019, 36(3): 515−523.
[4]

MOHD ZAKI N A, LATIF Z A, SURATMAN M N. Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates [J]. International Journal of Remote Sensing, 2018, 39(8): 2312−2340.
[5] 于贵瑞. 全球变化与陆地生态系统碳循环和碳蓄积[M]. 北京: 气象出版社, 2003.

YU Guirui. Golbal Change, Carbon Cycle and Storage in Terrestrial Ecosystem[M]. Beijing: China Meteorological Press, 2003.
[6] 张志, 田昕, 陈尔学, 等. 森林地上生物量估测方法研究综述[J]. 北京林业大学学报, 2011, 33(5): 144−150.

ZHANG Zhi, TIAN Xin, CHEN Erxue, et al. Review of methods on estimating forest above ground biomass [J]. Journal of Beijing Forestry University, 2011, 33(5): 144−150.
[7]

LU Dengsheng, CHEN Qi, WANG Guangxing, et al. Aboveground forest biomass estimation with landsat and LiDAR data and uncertainty analysis of the estimates[J/OL]. International Journal of Forestry Research, 2012, 2012: 436537[2025-07-05]. DOI: 10.1155/2012/436537.
[8]

CHEN Qi. LiDAR remote sensing of vegetation biomass[M]//WANG Guangxing, WENG Qihao. Remote Sensing of Natural Resources. Boca Raton: CRC Press, 2013: 424−445.
[9] 徐小军, 周国模, 杜华强, 等. 基于Landsat TM数据估算雷竹林地上生物量[J]. 林业科学, 2011, 47(9): 1−6.

XU Xiaojun, ZHOU Guomo, DU Huaqiang, et al. Estimation of aboveground biomass of Phyllostachys praecox forest based on Landsat thematic mapper image [J]. Scientia Silvae Sinicae, 2011, 47(9): 1−6.
[10] 徐婷, 曹林, 佘光辉. 基于Landsat 8 OLI的特征变量优化提取及森林生物量反演[J]. 遥感技术与应用, 2015, 30(2): 226−234.

XU Ting, CAO Lin, SHE Guanghui. Feature extraction and forest biomass estimation based on Landsat 8 OLI [J]. Remote Sensing Technology and Application, 2015, 30(2): 226−234.
[11] 井学辉, 曹磊, 刘云生, 等. 阿尔泰山小东沟乔木生物量空间分布规律[J]. 干旱区研究, 2016, 33(3): 511−518.

JING Xuehui, CAO Lei, LIU Yunsheng, et al. Spatial distribution pattern of biomass of Arbor species in Xiaodonggou in the Altay Mountains, China [J]. Arid Zone Research, 2016, 33(3): 511−518.
[12] 国庆喜, 张锋. 基于遥感信息估测森林的生物量[J]. 东北林业大学学报, 2003, 31(2): 13−16.

GUO Qingxi, ZHANG Feng. Estimation of forest biomass based on remote sensing [J]. Journal of Northeast Forestry University, 2003, 31(2): 13−16.
[13] 杨伟志, 赵鹏祥, 薛大庆, 等. 基于Landsat-8影像的西宁市南北山森林生物量估测模型研究[J]. 西北林学院学报, 2016, 31(2): 33−37, 97.

YANG Weizhi, ZHAO Pengxiang, XUE Daqing, et al. Estimation of forest biomass model of north and south mountains based on Landsat-8 remote sensing image data in Xining [J]. Journal of Northwest Forestry University, 2016, 31(2): 33−37, 97.
[14] 邱布布, 徐丽华, 张茂震, 等. 基于Landsat OLI和ETM+的杭州城市绿地地上生物量估算比较研究[J]. 西北林学院学报, 2018, 33(1): 225−232.

QIU Bubu, XU Lihua, ZHANG Maozhen, et al. Estimation of above-ground biomass of urban green land in Hangzhou based on Landsat OLI and ETM+ data [J]. Journal of Northwest Forestry University, 2018, 33(1): 225−232.
[15] 魏安超, 张大为. 云南松单木材积生长率模型研究[J]. 林业资源管理, 2020(6): 40−46.

WEI Anchao, ZHANG Dawei. Study on the volume growth rate model of Pinus yunnanensis of individual tree [J]. Forest Resources Management, 2020(6): 40−46.
[16] 李成荣, 王妍, 张超, 等. 基于GBRT模型的滇东地区森林碳储量遥感估算研究[J]. 三峡生态环境监测, 2023, 8(3): 96−106.

LI Chengrong, WANG Yan, ZHANG Chao, et al. Remote sensing estimation of forest carbon storage in Eastern Yunnan based on GBRT model [J]. Ecology and Environmental Monitoring of Three Gorges, 2023, 8(3): 96−106.
[17] 方精云, 刘国华, 徐嵩龄. 我国森林植被的生物量和净生产量[J]. 生态学报, 1996, 16(5): 497−508.

FANG Jingyun, LIU Guohua, XU Songling. Biomass and net production of forest vegetation in China [J]. Acta Ecologica Sinica, 1996, 16(5): 497−508.
[18] 罗恒春, 魏安超, 黄田, 等. 云南松生物量和碳储量动态变化分析[J]. 林业资源管理, 2016(6): 37−43.

LUO Hengchun, WEI Anchao, HUANG Tian, et al. Analysis on dynamic change of biomass and carbon stock of Pinus yunnanensis in Yunnan, China [J]. Forest Resources Management, 2016(6): 37−43.
[19] 卢腾飞, 胥辉, 欧光龙. 基于混合效应模型的曲靖市云南松林地上生物量遥感估测[J]. 西南林业大学学报(自然科学), 2020, 40(1): 104−115.

LU Tengfei, XU Hui, OU Guanglong. Remote sensing estimation on aboveground biomass for Pinus yunnanensis forests in Qujing City using mixed effect models [J]. Journal of Southwest Forestry University (Natural Sciences), 2020, 40(1): 104−115.
[20] 赵盼盼. 基于Landsat TM和ALOS PALSAR数据的森林地上生物量估测研究[D]. 杭州: 浙江农林大学, 2016.

ZHAO Panpan. Aboveground Forest Biomass Estimation Based on Landsat TM and ALOS PALSAR Data[D]. Hangzhou: Zhejiang A&F University, 2016.
[21] 孙雪莲. 基于Landsat 8-OLI的香格里拉高山松林生物量遥感估测模型研究[D]. 昆明: 西南林业大学, 2016.

SUN Xuelian. Biomass Estimation Model of Pinus densata Forests in Shangri-La City Based on Landsat 8-OLI by Remote Sensing[D]. Kunming: Southwest Forestry University, 2016.
[22] 马俊鹏. 基于多源哨兵卫星影像的湿地遥感制图研究[D]. 哈尔滨: 黑龙江大学, 2024.

MA Junpeng. Wetland Remote Sensing Mapping Based on Multi-source Sentinel Satellite Imagery[D]. Harbin: Helongjiang University, 2024.
[23]

BREIMAN L. Random forests [J]. Machine Learning, 2001, 45(1): 5−32.
[24]

XU Ruize, ZHANG Jiahua, WANG Jingwen, et al. Quantitative assessment of factors influencing the spatiotemporal variation in carbon dioxide fluxes simulated by multi-source remote sensing data in tropical vegetation[J/OL]. Remote Sensing, 2023, 15(24): 5677[2025-07-05]. DOI: 10.3390/rs15245677.
[25]

CHEN Tianqi, GUESTRIN C. XGBoost: a scalable tree boosting system[C]// KRISHNAPURAM B, SHAH M. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: Association for Computing Machinery, 2016: 785−794.
[26]

FRIEDMAN J H. Greedy function approximation: a gradient boosting machine [J]. The Annals of Statistics, 2001, 29(5): 1189−1232.
[27] 翁剑成, 付宇, 林鹏飞, 等. 基于梯度推进决策树的日维度交通指数预测模型[J]. 交通运输系统工程与信息, 2019, 19(2): 80−85, 93.

WENG Jiancheng, FU Yu, LIN Pengfei, et al. GBDT method based on prediction model of daily dimension traffic index [J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(2): 80−85, 93.
[28]

LIANG Xiao, XIE Qiwei, LUO Chunyan, et al. Principal model analysis based on bagging PLS and PCA and its application in financial statement fraud [J]. Journal of Systems Science and Information, 2024, 12(2): 212−228.
[29] 李春华. 升金湖枯水期草滩植被碳储量遥感估算研究[D]. 合肥: 安徽大学, 2021.

LI Chunhua. Remote Sensing Estimation of Grassland Carbon Storage During Dry Season at Shengjin Lake[D]. Hefei: Anhui University, 2021.
[30] 娄明华, 张会儒, 雷相东, 等. 天然栎类阔叶混交林林分平均高与平均胸径关系模型[J]. 北京林业大学学报, 2020, 42(9): 37−50.

LOU Minghua, ZHANG Huiru, LEI Xiangdong, et al. Relationship model between stand mean height and mean DBH for natural Quercus spp. broadleaved mixed stands [J]. Journal of Beijing Forestry University, 2020, 42(9): 37−50.
[31] 徐兴. 智能算法在 PID 失调时间预测中的应用[J]. 石化技术, 2025, 32(4): 209−211.

XU Xing. Application of intelligent algorithm in PID maladjustment time prediction [J]. Petrochemical Industry Technology, 2025, 32(4): 209−211.
[32]

LUO Mi, WANG Yifu, XIE Yunhong, et al. Combination of feature selection and CatBoost for prediction: the first application to the estimation of aboveground biomass[J/OL]. Forests, 2021, 12(2): 216[2025-07-05]. DOI: 10.3390/f12020216.
[33]

ZHANG Jialong, LU Chi, XU Hui, et al. Estimating aboveground biomass of Pinus densata -dominated-dominated forests using Landsat time series and permanent sample plot data [J]. Journal of Forestry Research, 2019, 30(5): 1689−1706.
[34] 史川, 胥辉, 张成程. 滇中地区针叶林碳储量遥感反演及动态变化分析[J]. 现代农业研究, 2025, 31(3): 83−85.

SHI Chuan, XU Hui, ZHANG Chengcheng. Remote sensing inversion and dynamic change analysis of carbon storage in coniferous forests in Central Yunnan [J]. Modern Agriculture Research, 2025, 31(3): 83−85.
[35] 陶云, 张万诚, 段长春, 等. 云南2009—2012年4年连旱的气候成因研究[J]. 云南大学学报(自然科学版), 2014, 36(6): 866−874.

TAO Yun, ZHANG Wancheng, DUAN Changchun, et al. Climatic causes of continuous drought over Yunnan Province from 2009 to 2012 [J]. Journal of Yunnan University (Natural Sciences Edition), 2014, 36(6): 866−874.
[36]

ZHANG Yuzhen, LIU Jingjing, LI Wenhao, et al. A proposed ensemble feature selection method for estimating forest aboveground biomass from multiple satellite data[J/OL]. Remote Sensing, 2023, 15(4): 1096[2025-07-05]. DOI: 10.3390/rs15041096.
[37] 岳彩荣. 香格里拉县森林生物量遥感估测研究[D]. 北京: 北京林业大学, 2012.

YUE Cairong. Forest Biomass Estimation in Shangri-La County Based on Remote Sensing[D]. Beijing: Beijing Forestry University, 2012.
[38] 张加龙, 胥辉. 基于遥感的高山松连清固定样地地上生物量估测模型构建[J]. 北京林业大学学报, 2020, 42(7): 1−11.

ZHANG Jialong, XU Hui. Establishment of remote sensing based model to estimate the aboveground biomass of Pinus densata for permanent sample plots from national forestry inventory [J]. Journal of Beijing Forestry University, 2020, 42(7): 1−11.
[39] 孙梦莲, 余坤勇, 张晓萍, 等. 基于机载激光雷达点云数据和Catboost算法的杉木单木蓄积量估测研究[J]. 西南林业大学学报 (自然科学), 2024, 44(3): 157−165.

SUN Menglian, YU Kunyong, ZHANG Xiaoping, et al. Research on estimation of single wood accumulation of chinese fir based on airborne Lidar point cloud data and Catboost algorithm [J]. Journal of Southwest Forestry University (Natural Sciences), 2024, 44(3): 157−165.