Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data

Wang Xinmiao; Zhao Feng; Liu Hua; Ling Chengxing; Liu Xia; Zeng Haowei

doi:10.12171/j.1000-1522.20240185

Journal of Beijing Forestry University > 2025 > 47(2): 143-151. > DOI: 10.12171/j.1000-1522.20240185

Wang Xinmiao, Zhao Feng, Liu Hua, Ling Chengxing, Liu Xia, Zeng Haowei. Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data[J]. Journal of Beijing Forestry University, 2025, 47(2): 143-151. DOI: 10.12171/j.1000-1522.20240185

Citation:

PDF (6261 KB)

Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data

Wang Xinmiao^{1, 2, 3,},
Zhao Feng^{1, 2, 3, ,},
Liu Hua^{1, 2, 3},
Ling Chengxing^{1, 2, 3},
Liu Xia^{1, 2},
Zeng Haowei^{1, 2}

1.
Institute of Resource Information, Chinese Academy of Forestry, Beijing 100091, China
2.
Key Laboratory of Forestry Remote Sensing and Information Technology, National Forestry and Grassland Administration, Beijing 100091, China
3.
Zhanjiang National Research Station for Mangrove Wetland Ecosystem, Zhanjiang 524448, Guangdong, China

More Information

Received Date: June 06, 2024
Revised Date: August 28, 2024
Available Online: February 16, 2025

Graphical Abstract

Abstract

Abstract

Objective
This paper aims to quickly and accurately obtain mangrove fractional canopy coverage based on unmanned aerial vehicle (UAV) LiDAR data, which would provide important reference for evaluating the effectiveness of mangrove ecological restoration.
Method
The location of this research is on Lingtou Island in Taiping Town, Zhanjiang City of Guangdong Province, southern China. Based on ULS data and ground field data of 40 sample plots, linear regression was used to fit the measured and estimated fractional coverage of mangrove, and the determination coefficient (R²), root mean square error (RMSE) and estimation accuracy (E_A) were calculated. The estimation accuracies of mangrove fractional coverage through four different algorithms based on the first return proportionality model (FRRM), the all return proportionality model (ARRM), pulse return intensity proportionality model (PRIRM), and canopy height model (CHM) were compared. The correlations between sample site fractional coverage, sample site laser point cloud density, sample site LiDAR height characteristic variable and fractional coverage estimation error were analyzed. Finally, the optimal coverage estimation model with the best accuracy was selected to estimate mangrove coverage in the study area, and mapping was carried out.
Result
(1) The estimation accuracy of mangrove fractional coverage based on FRRM model was the highest (R² = 0.970 1, RMSE = 0.032 5, E_A = 93.01%), and the estimation error was the lowest with an average underestimation of 1.04%. The second was the algorithm based on ARRM model (R² = 0.977 4, RMSE= 0.033 6, E_A = 92.58%), the third was the algorithm based on CHM model (R² = 0.945 0, RMSE = 0.044 0, E_A = 90.54%), and the accuracy of PRIRM model was the lowest (R² = 0.9509, RMSE = 0.0610, E_A = 88.17%). (2) The estimation errors of PRIRM model were significantly negatively correlated with both coverage and height characterization variables, and were generally overestimated. Meanwhile, there was no significant correlation between sample site fractional coverage and estimation error of algorithm based on FRRM, ARRM and CHM models. (3) The results of LiDAR sampling sensitivity analysis showed that a raster size of 3 m was most suitable as a raster unit for mapping the coverage of the study area.
Conclusion
The estimation accuracy of mangrove fractional coverage based on the four models is high, and the estimation accuracy of fractional coverage based on FRRM model could be the highest, the estimation results are reliable, which can provide support for the scientific management and ecological restoration of mangrove forests on Lingtou Island, Guangdong Province of southern China.
- unmanned aerial vehicle (UAV) LiDAR,
- point cloud,
- first return,
- mangrove forest,
- coverage

FullText(HTML)

References (35)

References

[1]	Chen L, Wang W, Zhang Y, et al. Recent progresses in mangrove conservation, restoration and research in China[J]. Journal of Plant Ecology, 2009, 2(2): 45−54. doi: 10.1093/jpe/rtp009
[2]	Ma M, Wang Q, Liu R, et al. Effects of climate change and human activities on vegetation coverage change in northern China considering extreme climate and time-lag and accumulation effects[J]. Science of the Total Environment, 2023, 860: 160527. doi: 10.1016/j.scitotenv.2022.160527
[3]	Zhou M, Huang Y, Li G. Changes in the concentration of air pollutants before and after the COVID-19 blockade period and their correlation with vegetation coverage[J]. Environmental Science and Pollution Research, 2021, 28(18): 23405−23419. doi: 10.1007/s11356-020-12164-2
[4]	Zuo Y, Li Y, He K, et al. Temporal and spatial variation characteristics of vegetation coverage and quantitative analysis of its potential driving forces in the Qilian Mountains, China, 2000−2020[J]. Ecological Indicators, 2022, 143: 409−429.
[5]	朱林富. 基于MODIS数据的四川植被覆盖度景观格局特征分析 [J]. 乐山师范学院学报, 2022, 37(4): 45−51, 83. Zhu L F. Analysis on the landscape pattern characteristics of fractional vegetation coverage based on MODlS data in Sichuan [J]. Journal of Leshan Normal University, 2022, 37(4): 45−51, 83.
[6]	Piao S, Ciais P, Friedlingstein P, et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming[J]. Nature, 2008, 451: 49−52. doi: 10.1038/nature06444
[7]	李钰溦, 贾坤, 魏香琴, 等. 中国北方地区植被覆盖度遥感估算及其变化分析[J]. 国土资源遥感, 2015, 27(2): 112−117. Li Y W, Jia K, Wei X Q, et al. Fractional vegetation cover estimation in northern China and its change analysis[J]. Remote Sensing of Land and Resources, 2015, 27(2): 112−117.
[8]	刘二华, 周广胜, 周莉. 基于文献整合的中国不同下垫面植被覆盖度遥感估算模型数据集[J]. 中国科学数据(中英文网络版), 2019, 4(4): 176−184. Liu E H, Zhou G S, Zhou L. A literature-based dataset of fractional vegetation cover remote sensince stimation models for different underlying surfaces in China[J]. Scientific Data in China, 2019, 4(4): 176−184.
[9]	Yan G, Li L, Coy A, et al. Improving the estimation of fractional vegetation cover from UAV RGB imagery by colour unmixing[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2019, 158: 23−34. doi: 10.1016/j.isprsjprs.2019.09.017
[10]	刘琳, 郑兴明, 姜涛, 等. 无人机遥感植被覆盖度提取方法研究综述[J]. 东北师大学报(自然科学版), 2021, 53(4): 151−160. Liu L, Zheng X M, Jiang T, et al. Extraction method of fractional vegetation cover from unmanned aerial vehicle remote sensing: a review[J]. Journal of Northeast Normal University (Natural Science Edition), 2021, 53(4): 151−160.
[11]	Coops N C, Hilker T, Wulder M A, et al. Estimating canopy structure of Douglas-fir forest stands from discrete-return LiDAR[J]. Trees-Structure and Function, 2007, 21(3): 295−310. doi: 10.1007/s00468-006-0119-6
[12]	Falkowski M J, Smith A M S, Gessler P E, et al. The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data[J]. Canadian Journal of Remote Sensing, 2008, 34: S338−S350. doi: 10.5589/m08-055
[13]	Ma Q, Su Y, Guo Q. Comparison of canopy cover estimations from airborne LiDAR, aerial imagery, and satellite imagery[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017: 1−12. doi: 10.1109/JSTARS.2017.2711482.
[14]	Wasser L, Day R, Chasmer L, et al. Influence of vegetation structure on Lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions[J]. PLoS One, 2013, 8(1): e54776. doi: 10.1371/journal.pone.0054776
[15]	Cui Y, Zhao K, Fan W, et al. Retrieving crop fractional cover and LAI based on airborne Lidar data[J]. Journal of Remote Sensing, 2011, 15(6): 1276−1288.
[16]	Riano D, Meier E, Allgöwer B, et al. Modeling airborne laser scanning data for the spatial generation of critical forest parameters in fire behavior modeling[J]. Remote Sensing of Environment, 2003, 86(2): 177−186. doi: 10.1016/S0034-4257(03)00098-1
[17]	Hopkinson C, Chasmer L. Testing LiDAR models of fractional cover across multiple forest ecozones[J]. Remote Sensing of Environment, 2009, 113(1): 275−288. doi: 10.1016/j.rse.2008.09.012
[18]	Kato A, Moskal L M, Schiess P, et al. Capturing tree crown formation through implicit surface reconstruction using airborne lidar data[J]. Remote Sensing of Environment, 2009, 113(6): 1148−1162. doi: 10.1016/j.rse.2009.02.010
[19]	Li C, Zheng Y, Zhang X, et al. Comparison of canopy cover and leaf area index estimation from airborne LiDAR and digital aerial potogrammetry in tropical forests[J]. Applied Sciences, 2022, 12(19): 9882. doi: 10.3390/app12199882
[20]	Angiulli F, Basta S, Pizzuti C. Distance-based detection and prediction of outliers[J]. IEEEE Transactions on Knowledge and Data Engineering, 2006, 18(2): 145−160. doi: 10.1109/TKDE.2006.29
[21]	张昌赛, 刘正军, 杨树文, 等. 基于LiDAR数据的布料模拟滤波算法的适用性分析[J]. 激光技术, 2018, 42(3): 410−416. doi: 10.7510/jgjs.issn.1001-3806.2018.03.023 Zhang C S, Liu Z J, Yang S W, et al. Applicability analysis of cloth simulation filtering algorithm based on LiDAR data[J]. Laser Technology, 2018, 42(3): 410−416. doi: 10.7510/jgjs.issn.1001-3806.2018.03.023
[22]	国家测绘地理信息局. 机载激光雷达数据获取技术规范: CH/T8024−2011[S]. 北京: 国家测绘地理信息局, 2011: 1−17. National Administration of Surveying, Specifications for data acquisition of airbore LIDAR : CH/T 8024−2011 [S]. Beijing: China Standard Press, 2011: 1−17.
[23]	陈祖刚, 巴图娜存, 徐芝英, 等. 基于数码相机的草地植被盖度测量方法对比研究[J]. 草业学报, 2014, 23(6): 20−27. doi: 10.11686/cyxb20140603 Chen Z G, Batunacun, Xu Z Y, et al. Measuring grassland vegetation cover using digital camera images[J]. Journal of Pratacultural Science, 2014, 23(6): 20−27. doi: 10.11686/cyxb20140603
[24]	Korhonen L, Korpela I, Heiskanen J, et al. Airborne discrete-return LIDAR data in the estimation of vertical canopy cover, angular canopy closure and leaf area index[J]. Remote Sensing of Environment, 2011, 115(4): 1065−1080. doi: 10.1016/j.rse.2010.12.011
[25]	Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research-Atmospheres, 2001, 106(D7): 7183−7192. doi: 10.1029/2000JD900719
[26]	谢栋博, 雷雅凯, 张宇超, 等. 基于机载LiDAR数据的崇礼冬奥核心区树冠覆盖率估算[J]. 林业科学, 2022, 58(10): 24−34. Xie D B, Lei Y K, Zhang Y C, et al. Estimation of canopy cover in the core area of Winter Olympic Games based on airborne LiDAR data[J]. Scientia Silvae Sinicae, 2022, 58(10): 24−34.
[27]	Zhao K, Popescu S, Meng X, et al. Characterizing forest canopy structure with lidar composite metrics and machine learning[J]. Remote Sensing of Environment, 2011, 115(8): 1978−1996. doi: 10.1016/j.rse.2011.04.001
[28]	Hopkinson C. The influence of flying altitude, beam divergence, and pulse repetition frequency on laser pulse return intensity and canopy frequency distribution[J]. Canadian Journal of Remote Sensing, 2007, 33(4): 312−324. doi: 10.5589/m07-029
[29]	Liu Q W, Fu L Y, Wang G, et al. Improving estimation of forest canopy cover by introducing loss ratio of laser pulses using airborne LiDAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(1): 567−585. doi: 10.1109/TGRS.2019.2938017
[30]	Jakubowski M K, Guo Q, Kelly M. Tradeoffs between lidar pulse density and forest measurement accuracy[J]. Remote Sensing of Environment, 2013, 130: 245−253. doi: 10.1016/j.rse.2012.11.024
[31]	Vauhkonen J, Tokola T, Maltamo M, et al. Effects of pulse density on predicting characteristics of individual trees of Scandinavian commercial species using alpha shape metrics based on airborne laser scanning data[J]. Canadian Journal of Remote Sensing, 2008, 34(Suppl. 2): S441−S459. doi: 10.5589/m08-052
[32]	刘鹤, 顾玲嘉, 任瑞治. 基于无人机遥感技术的森林参数获取研究进展[J]. 遥感技术与应用, 2021, 36(3): 489−501. Liu H, Gu L J, Ren R Z. Research progress of forest parameter acquisition based on UAV remote sensing technology[J]. Remote Sensing Technology and Application, 2021, 36(3): 489−501.
[33]	秦海明, 王成, 习晓环, 等. 机载激光雷达测深技术与应用研究进展 [J]. 遥感技术与应用, 2016, 31(04): 617−624. Qin H M, Wang C, Xi X H, et al. Development of airborne laser bathymetric technology and applications [J]. Remote Sensing Technology and Application, 2016, 31(4): 617−624.
[34]	张瑞英, 庞勇, 李增元, 等. 结合机载LiDAR和LANDSAT ETM+ 数据的温带森林郁闭度估测 [J]. 植物生态学报, 2016, 40(2): 102−115. Zhang R Y, Pang Y, Li Z Y, et al. Canopy closure estimation in a temperate forest using airborne LiDAR and LANDSAT ETM + data [J]. Chinese Journal of Plant Ecology, 2016, 40(2): 102−115.
[35]	Hershey J L, McDill M E, Miller D A, et al. A voxel-based individual tree stem detection method using airborne LiDAR in mature northeastern US forests[J]. Remote Sensing, 2022, 14(3): 806. doi: 10.3390/rs14030806

[1]	Luo Ye, Wang Jun, Yang Yuchun, He Huaijiang, Yu Haiou, Zheng Jun, Zhang Tianxiang. DBH class structure and arbor biomass of Juglans mandshurica secondary forest[J]. Journal of Beijing Forestry University, 2022, 44(1): 29-37. DOI: 10.12171/j.1000-1522.20200348
[2]	Qin Qianqian, Wang Haiyan, Zheng Yonglin, Lei Xiangdong. Spatial distribution characteristics of litter nutrients in temperate spruce-fir mixed forests[J]. Journal of Beijing Forestry University, 2021, 43(3): 73-84. DOI: 10.12171/j.1000-1522.20200065
[3]	Chen Bei-bei, Wang Kai, Ni Rui-qiang, Cheng Yan-xia. Composition and spatial pattern of tree seedlings in a coniferous and broadleaved mixed forest in Changbai Mountain of northeastern China[J]. Journal of Beijing Forestry University, 2018, 40(2): 68-75. DOI: 10.13332/j.1000-1522.20170370
[4]	SUN Yue, XIA Fu-cai, HE Huai-jiang, LIU Bao-dong, WANG Ge-rong, LI Liang. Community structure features of the larch-birch secondary forest on the northeastern slope of Changbai Mountain, Northeast China.[J]. Journal of Beijing Forestry University, 2016, 38(12): 28-38. DOI: 10.13332/j.1000-1522.20160088
[5]	WANG Xin, ZHANG Hua-yu, LI Zong-feng, ZHANG Shi-qiang, WANG Guo-hang, DENG Hong-ping. Community structure and population regeneration of an endangered plant, Thuja sutchuenensis.[J]. Journal of Beijing Forestry University, 2016, 38(10): 28-37. DOI: 10.13332/j.1000-1522.20160028
[6]	L&#xDC, Xun, YANG Shuang-bao, LIU Wen-zhen, GUO Xiao-long, LI An-min, YUAN Yi-chao. Analysis of spatial structure characteristics based on diameter class of trees in primeval Quercus aliena var. acuteserrata community in the forest area of Xiaolong Mountain, Gansu Province.[J]. Journal of Beijing Forestry University, 2015, 37(5): 11-18. DOI: 10.13332/j.1000-1522.20140238
[7]	LUO Zong-shi, XIANG Cheng-hua, ZHANG Lu, XIE Da-jun, LUO Xiao-hua. Spatial distribution of fine roots and underground competition between Chinese prickly ash (Zanthoxylum bungenum) and weeds in Chinese prickly ash plantation[J]. Journal of Beijing Forestry University, 2010, 32(2): 86-91.
[8]	HAN Lu, WANG Hai-zhen, PENG Jie, LIANG Ji-ye, MA Chun-hui. Size-class structure and distribution pattern of Populus euphratica Oliv. in different habitats.[J]. Journal of Beijing Forestry University, 2010, 32(1): 7-12.
[9]	MAO Lei, , WANG Dong-mei, YANG Xiao-hui, YU Hong. Spatial patterns of young Pinus sylvestris var. mongolica saplings and their regeneration analysis in different stands of Inner Mongolia, northern China.[J]. Journal of Beijing Forestry University, 2008, 30(6): 71-77.
[10]	MA Yu-fei, LI Jun-qing. Population structure of Davidia involucrate in Mt.Seven-sister Nature Reserve of central China’s Hubei Province[J]. Journal of Beijing Forestry University, 2005, 27(3): 12-16.

Cited By

Cited by

Periodical cited type(62)

1.	张聪，刘琪，李海奎，刘鹏举，詹思颖. 我国尺度兼容和树种分类的材积源森林碳储量模型. 林业科学. 2025(01): 57-69 .
2.	衣娜娜，毕力格，史金丽，蔡敏，许志丽，郑凤杰，丽娜. 内蒙古大兴安岭飞机冷云增雨潜势预报模型. 干旱区研究. 2025(03): 409-419 .
3.	王伟武，伏添乐，陈欢. 基于PLUS-InVEST模型的长三角城市群碳储量时空演变与预测. 环境科学. 2025(04): 1937-1950 .
4.	徐思若，成志影，那雪迎，张栩嘉，马大龙，张鹏. 黑龙江省森林碳汇及其经济价值的变化分析与潜力预测. 生态学杂志. 2024(01): 197-205 .
5.	张圆圆，龙勤. 云南省森林净碳储量估算及影响因素分析——基于碳源和碳汇双重角度. 绿色科技. 2024(01): 227-231 .
6.	汪宗顺，张海鹏，岳超，杨红强，张寒. 造林增汇是实现碳中和的成本有效途径吗？——以西北地区为例. 自然资源学报. 2024(03): 731-748 .
7.	田震，高凡，赛硕，杨之恒，丁国栋. 清水河县森林生态系统碳储量、碳密度分布特征. 干旱区资源与环境. 2024(06): 166-173 .
8.	杨俊豪，张皓东，李永昌，刘书敏. 基于可加性模型的云南松和华山松碳储量模型构建. 昆明理工大学学报(自然科学版). 2024(02): 140-150 .
9.	于鲁冀，张亚慧，樊雷，王莉，刘莹莹. 河南省森林固碳量与碳汇价值评估. 郑州大学学报(工学版). 2024(03): 7-13 .
10.	袁慧兰，郑甜甜，林佳敏，鲍雪莲，闵凯凯，朱雪峰，解宏图，梁超. 农林土壤置换对植物残体分解过程的影响. 生态学杂志. 2024(04): 1017-1024 .
11.	李林，赵毅，温智峰，刘佳润，魏识广，周景钢，冯嘉谊. 南亚热带常绿阔叶林地上碳储量空间分布特征及其影响因素. 生态学报. 2024(11): 4687-4697 .
12.	马振华. 基于第7至9次森林资源清查的宁夏森林碳储量动态变化特征. 陕西林业科技. 2024(03): 39-45 .
13.	郭同方，吴水荣，张超，苏秦. 重点国有林区森林碳储量动态变化及增汇策略分析——以龙江森工为例. 江西农业大学学报. 2024(03): 582-596 .
14.	贺晨瑞，庞丽峰，谭炳香，黄逸飞，孙学霞. 基于遥感的北京市森林地上碳储量监测. 西北林学院学报. 2024(03): 162-170+265 .
15.	Zhencan ZHENG，Liuwen ZHUANG，Guofang MIAO，Han LIU，Zhiqiang CHENG，Wenyu LI，Rong SHANG，Peng GONG，Jing Ming CHEN. Elevational distribution of forests and its spatiotemporal dynamics in subtropical China from 2000 to 2019. Science China Earth Sciences. 2024(08): 2563-2582 .
16.	郑振灿，庄留文，缪国芳，刘涵，程志强，李纹宇，商荣，宫鹏，陈镜明. 中国亚热带地区2000～2019年森林海拔分布特征及其时空动态. 中国科学:地球科学. 2024(08): 2604-2624 .
17.	张亮. 韶关市森林植被碳储量与碳密度动态研究. 林业与环境科学. 2024(05): 88-94 .
18.	范彩慧，段成波，龙德增，赵润华. 保山市森林生物质碳储量分析. 绿色科技. 2024(20): 68-73 .
19.	胡兴波，张月，胡长江，李慧波，王光鑫. 西南地区常见林木的固碳经济效益分析. 农村科学实验. 2024(23): 114-116 .
20.	曹爱平，罗雷，王晓荣，徐立，郑晓敏. 基于湖北第五次森林资源普查数据的森林碳储量研究. 湖北林业科技. 2024(06): 1-6 .
21.	王媛媛，刘可欣，张湘婕，王艺璇，赵俊玮，徐连杰，郭婷婷，王天意，任志彬，代新竹. 城市森林碳汇估算计量——以长春市为例. 中国城市林业. 2024(06): 116-122 .
22.	张翔，刘洋，玉山，苏日娜，阿茹汗. 基于无人机激光雷达和多光谱数据的森林树高提取方法研究. 森林工程. 2023(01): 29-36 .
23.	胡勐鸿，李万峰，吕寻. 日本落叶松自由授粉家系选择和无性繁殖利用. 温带林业研究. 2023(01): 7-16 .
24.	陈宏福，韦体，敏正龙，妥永华，高丹丹，蔡勇，杨具田，白日霞，郭鹏辉. 黄河上游太子山国家级自然保护区森林碳储量及碳增汇潜力研究. 西北民族大学学报(自然科学版). 2023(01): 63-70 .
25.	朱建华，田宇，李奇，刘华妍，郭学媛，田惠玲，刘常富，肖文发. 中国森林生态系统碳汇现状与潜力. 生态学报. 2023(09): 3442-3457 .
26.	张煜星，王雪军. 1973—2018年我国桉树人工林生产力及碳汇能力. 林业科学. 2023(03): 54-64 .
27.	简尊吉，朱建华，王小艺，肖文发. 我国陆地生态系统碳汇的研究进展和提升挑战与路径. 林业科学. 2023(03): 12-20 .
28.	刘川. 利用森林资源一类清查数据计算活立木蓄积量的方法及应用研究. 山东林业科技. 2023(03): 69-72 .
29.	冯源，王连晶. 县域尺度乔木林碳储量及碳汇特征分析——以马关县为例. 西部林业科学. 2023(03): 152-159 .
30.	罗佩文，熊小雨，刘冰琳. 基于机器学习和生长预测的森林固碳分析和多目标规划管理策略. 科技风. 2023(19): 156-159 .
31.	汤浩藩，季巍，周卫平，欧阳希，王华，张文晶，许彦红. 主要乔木树种碳储量与碳密度分布特征——以昆明市海口林场为例. 南方林业科学. 2023(03): 6-10+29 .
32.	王迎辉，孙浩然，解华臻，张岩，卢振生. 小型树苗扦插机的设计. 南方农机. 2023(22): 35-37+57 .
33.	江晓雨，刘康桢，徐帅. 基于LCA的合肥滨湖国家森林公园的环境评估. 合肥学院学报(综合版). 2023(05): 70-76 .
34.	李放，孙红召，黄新峰，曹文昱，冯东阳，冯瑞琦. 基于河南省第九次森林资源清查的乔木林碳储量研究. 绿色科技. 2023(17): 151-157 .
35.	陈晨，时军霞，刘光武. 河南省乔木林碳储量和碳密度研究. 安徽农业科学. 2023(22): 108-111 .
36.	韩艺，张峰. 北京市不同功能分区的乔木林储碳功能对比研究. 林业调查规划. 2023(05): 26-31 .
37.	朱昳橙. 海寨林场主要乔木树种碳储量和碳密度特征研究. 宁夏农林科技. 2023(12): 24-28 .
38.	张颖，李晓格. 碳达峰碳中和目标下北京市森林碳汇潜力分析. 资源与产业. 2022(01): 15-25 .
39.	徐明锋，苏永新，龚益广，王锋，张少杰，何春梅. 粤西桉阔林乔木层碳密度及其影响因子研究. 林业与环境科学. 2022(01): 1-8 .
40.	张全斌，周琼芳. “双碳”目标下中国能源CO_2减排路径研究. 中国国土资源经济. 2022(04): 22-30 .
41.	付晓，张煜星，王雪军. 2060年前我国森林生物量碳库及碳汇潜力预测. 林业科学. 2022(02): 32-41 .
42.	尹海龙，张乃暄，许中旗. 冀北坝上及坝下地区华北落叶松林土壤碳储量的比较. 林业与生态科学. 2022(02): 164-168 .
43.	刘华超，任春颖，王宗明，张柏. 大兴安岭生态功能区生态系统服务功能动态及权衡协同关系研究. 生态与农村环境学报. 2022(05): 587-598 .
44.	胡忠宇，苏建兰. 森林植被碳储量研究综述与展望. 农业与技术. 2022(12): 58-62 .
45.	肖水寒，张驭骁，张佳栋，周甲佳. 基于碳存储量预测的森林经营计划决策模型. 科学技术创新. 2022(23): 165-168 .
46.	Menghong HU，Jiying LI，Man SUN. Strong Seedlings of Improved Varieties and High-efficiency Cultivation of Artificial Forests Promotes the Early Realization of "Carbon Neutrality". Agricultural Biotechnology. 2022(04): 136-141 .
47.	曾丽，吕寻，胡勐鸿. 良种是加速实现“碳中和”的有效保障措施——以甘肃省地方良种为例. 林业科技通讯. 2022(08): 35-39 .
48.	周静，吕晓琪. 北京大兴区森林植被碳储量计算及价值分析. 绿色科技. 2022(16): 49-52 .
49.	薛春泉，陈振雄，杨加志，曾伟生，林丽平，刘紫薇，张红爱，苏志尧. 省市县一体化森林碳储量估测技术体系——以广东省为例. 林业资源管理. 2022(04): 157-163 .
50.	雷海清，孙高球，郑得利. 温州市森林生态系统碳储量研究. 南京林业大学学报(自然科学版). 2022(05): 20-26 .
51.	肖君. 福建省天然乔木林碳储量动态变化及增汇策略. 南京林业大学学报(自然科学版). 2022(05): 27-32 .
52.	范春楠，刘强，郑金萍，郭忠玲，张文涛，刘英龙，谢遵俊，任增君. 采伐强度对阔叶红松林生态系统碳密度恢复的影响. 北京林业大学学报. 2022(10): 33-42 . 本站查看
53.	张颖，孟娜，姜逸菲. 中国森林碳汇与林业经济发展耦合及长期变化特征分析. 北京林业大学学报. 2022(10): 129-141 . 本站查看
54.	陈科屹，王建军，何友均，张立文. 黑龙江大兴安岭重点国有林区森林碳储量及固碳潜力评估. 生态环境学报. 2022(09): 1725-1734 .
55.	荀文会. “碳中和”视角下的沈阳市国土空间规划路径. 规划师. 2022(10): 88-92 .
56.	王飞平，张加龙. 基于碳卫星的森林碳储量估测研究综述. 世界林业研究. 2022(06): 30-35 .
57.	侯瑞萍，陈健，夏朝宗，宋佳庚，黄翔，郑芊卉，安天宇，郝月兰. 2020年京津冀地区林地和其他生物质碳汇量研究. 林业资源管理. 2022(05): 42-52 .
58.	郭学媛，朱建华，刘华妍，田惠玲，李春蕾，刘常富，肖文发. 林业活动对区域森林生物量碳源汇格局的影响——以南平市为例. 生态学报. 2022(23): 9548-9559 .
59.	侯瑞萍，夏朝宗，陈健，郑芊卉，李海奎，黄金金，黄翔，邓继峰，韩旭，安天宇，郝月兰，苟丽晖. 长江经济带林地和其他生物质碳储量及碳汇量研究. 生态学报. 2022(23): 9483-9498 .
60.	韩光荣，赵琦，邵长城. 优化施肥对樟子松幼苗生长和抗逆生理的影响. 林业调查规划. 2022(06): 49-54 .
61.	刘丽，彭琛. 宁强县森林资源变化分析. 绿色科技. 2021(19): 97-99 .
62.	张峰，彭祚登. 北京市森林碳储量和碳汇经济价值研究. 林业资源管理. 2021(06): 52-58 .

Other cited types(41)

Get Citation

PDF

XML

Article views (127) PDF downloads (46) Cited by(103)

Turn off MathJax

Article Contents

Abstract

References

Estimating mangrove forest coverage based on unmanned aerial vehicle (UAV) LiDAR point cloud data