Remote Sensing Technology and Application, Volume. 40, Issue 3, 557(2025)
Estimating Forest Age of Temperate Forests in China based on Forest Canopy Height: A Case Study in Heilongjiang Province
[1] YANG Q, SU Y, HU T et al. Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes. Forest Ecosystems, 9, 100059(2022).
[2] ZHANG Ying, ZHOU Xue, QIN Qingfeng et al. Value accounting of forest carbon sinks in China. Journal of Beijing Forestry University, 35, 124-131(2013).
[3] LI Wenjuan, ZHAO Chuanyan, BIE Qiang et al. Retrieval of the forest structural parameters using Airborne LiDAR Data. Remote Sensing Technology and Application, 30, 917-924(2015).
[4] CAO Jixin, TIAN Yun, WANG Xiaoping et al. Estimation methods of forest sequestration and their prospects. Ecology and Environment, 18, 2001-2005(2009).
[5] ZHANG Y, YAO Y, WANG X et al. Mapping spatial distribution of forest age in China. Earth and Space Science, 4, 108-116(2017).
[6] YU Z, ZHAO H, LIU S et al. Mapping forest type and age in China's plantations. Science of The Total Environment, 744, 140790(2020).
[7] CHEN B, CAO J, WANG J et al. Estimation of rubber stand age in typhoon and chilling injury afflicted area with Landsat TM data:A case study in Hainan Island,China. Forest Ecology and Management, 274, 222-230(2012).
[8] CHEN B, JIN Y, BROWN P. Automatic mapping of planting year for tree crops with Landsat satellite time series stacks. ISPRS Journal of Photogrammetry and Remote Sensing, 151, 176-188(2019).
[9] ZHANG C, JU W, CHEN J M et al. Mapping forest stand age in China using remotely sensed forest height and observation data. Journal of Geophysical Research: Biogeosciences, 119, 1163-1179(2014).
[10] NIE S, ZHU X, WANG C et al. The global 30-m forest canopy height map for 2020. Zenodo(2023).
[11] POTAPOV P, LI X Y, HERNANDEZ-SERNA A et al. Mapping global forest canopy height through integration of GEDI and Landsat data. Remote Sensing of Environment, 253, 112165(2021).
[12] LIU X, SU Y, HU T et al. Neural network guided interpolation for mapping canopy height of China’s forests by integrating GEDI and ICESat-2 data. Remote Sensing of Environment, 269, 112844(2022).
[13] FANG J, YU G, LIU L et al. Climate change, human impacts, and carbon sequestration in China. Proceedings of the National Academy of Sciences, 115, 4015-4020(2018).
[15] LIN X, SHANG R, CHEN J M et al. High-resolution forest age mapping based on forest height maps derived from GEDI and ICESat-2 space-borne LiDAR data. Agricultural and Forest Meteorology, 339, 109592(2023).
[16] LIU L, XIAO Z, XIDONG C et al. GLC_FCS30-2020:Global Land Cover with Fine Classification System at 30m in 2020. Zenodo(2020).
[18] ZHOU Yanlian, JU Weimin, LIU Yibo. Characteristics and driving factors of global terrestrial ecosystem carbon fluxes from 1981 to 2019. Trans Atmos Sci, 45, 332-344(2022).
[20] HORN B K P. Hill shading and the reflectance map. Proceedings of the IEEE, 69, 14-47(1981).
[21] WARE G, OHKI K, MOON L. The Mitscherlich plant growth model for determining critical nutrient deficiency levels 1. Agronomy Journal, 74, 88-91(1982).
[22] ZEIDE B. Accuracy of equations describing diameter growth. Canadian Journal of Forest research, 19, 1283-1286(1989).
[23] FENG-RI L, BAO-DONG Z, GUI-LIN S. A derivation of the generalized Korf growth equation and its application. Journal of Forestry Research, 11, 81-88(2000).
[24] ROSSI S, DESLAURIERS A, MORIN H. Application of the Gompertz equation for the study of xylem cell development. Dendrochronologia, 21, 33-39(2003).
[25] RICKER W. Growth rates and models. Fish physiology, 677-744(1979).
[26] NEMANI R R, KEELING C D, HASHIMOTO H et al. Climate-Driven increases in Global Terrestrial Net primary production from 1982 to 1999. Science, 300, 1560-1563(2003).
[27] CHEN J, YANG H, MAN R et al. Using machine learning to synthesize spatiotemporal data for modelling DBH-height and DBH-height-age relationships in boreal forests. Forest Ecology and Management, 466, 118104(2020).
[28] PRETZSCH H, DEL RÍO M, ARCANGELI C et al. Forest growth in Europe shows diverging large regional trends. Scientific Reports, 13, 15373(2023).
[29] LEUSCHNER C, BAT-ENEREL B. Effects of heat,elevated vapor pressure deficits and growing season length on growth trends of European beech. Frontiers in Forests and Global Change, 7(2024).
[30] KHOSRAVIPOUR A, SKIDMORE A K, WANG T et al. Effect of slope on treetop detection using a LiDAR Canopy Height Model. ISPRS Journal of Photogrammetry and Remote Sensing, 104, 44-52(2015).
[31] ZHU X, NIE S, WANG C et al. Consistency analysis of forest height retrievals between GEDI and ICESat-2. Remote Sensing of Environment, 281, 113244(2022).
[32] SU R, WU Q, YANG Y et al. Relationship between diameter at breast height and tree age in populations of a rare and endangered plant, Davidia involucrata. Polish Journal of Ecology, : 69, 84-95(2021).
[33] FERNÁNDEZ-MARTÍNEZ M, VICCA S, JANSSENS I A et al. Spatial variability and controls over biomass stocks, carbon fluxes, and resource-use efficiencies across forest ecosystems. Trees, 28, 597-611(2014).
[34] YUAN W, ZHENG Y, PIAO S et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Science Advances, 5(2019).
Get Citation
Copy Citation Text
Chengyu LIU, Qin MA, Yanlian ZHOU, Weimin JU. Estimating Forest Age of Temperate Forests in China based on Forest Canopy Height: A Case Study in Heilongjiang Province[J]. Remote Sensing Technology and Application, 2025, 40(3): 557
Category:
Received: Feb. 2, 2024
Accepted: --
Published Online: Sep. 28, 2025
The Author Email: Yanlian ZHOU (zhouyl@nju.edu.cn)