Canopy spectral characteristics of broadleaved Korean pine forest in different successional stages and its relation with temperature in Changbai Mountain of northeastern China

Wang Yaoyao; Zhou Guang; Liu Qijing; Zhou Yang

doi:10.12171/j.1000-1522.20200373

Journal of Beijing Forestry University > 2021 > 43(7): 40-53. > DOI: 10.12171/j.1000-1522.20200373

Wang Yaoyao, Zhou Guang, Liu Qijing, Zhou Yang. Canopy spectral characteristics of broadleaved Korean pine forest in different successional stages and its relation with temperature in Changbai Mountain of northeastern China[J]. Journal of Beijing Forestry University, 2021, 43(7): 40-53. DOI: 10.12171/j.1000-1522.20200373

Citation:

PDF (10785 KB)

Canopy spectral characteristics of broadleaved Korean pine forest in different successional stages and its relation with temperature in Changbai Mountain of northeastern China

1.
Key Laboratory of Forest Resources and Environment of State Forestry and Grassland Administration , Beijing Forestry University, Beijing 100083, China
2.
Jiangxi Academy of Forestry, Nanchang 330013, Jiangxi, China

More Information

Received Date: November 28, 2020
Revised Date: January 19, 2021
Available Online: April 19, 2021
Published Date: July 24, 2021

Graphical Abstract

Abstract

Abstract

Objective Based on remote sensing data, the characteristics of canopy spectral changes in different succession stages of broadleaved Korean pine forest in Changbai Mountain of northeastern China were analyzed to provide theoretical basis for revealing the interspecies change and the response mechanism of vegetation productivity to climate factors in Changbai Mountain.
Method Through the Google Earth Engine platform, Landsat and Sentinel series of remote sensing images were used to extract multi-temporal canopy spectrum data for the broadleaved Korean pine forest (primary forest) and birch-aspen forest (secondary forest), both were in a same succession series in Changbai Mountain. Also we analyzed the seasonal variations of the canopy spectrum characteristics of the two, the seasonal and inter-annual variation of vegetation greenness, and calculated the Pearson correlation coefficient between the inter-annual vegetation greenness variation and the monthly average temperature of the same period from 1985 to 2019.
Result (1) For canopy spectral reflectance of the primary forest, the visible light was higher in leaf-off season than in growing season, while the near-infrared reflectance showed an opposite pattern. In the vigorous growth season (from the end of May to the end of October), the canopy reflectivity of the primary forest and the secondary forest was similar in the visible light band, but the near-infrared band was significantly different, and the secondary forest canopy reflectivity was higher. The phenomenon of “red valley”, “green peak”, “blue valley” and “red edge”, the curve form of spectral reflectance in the two vegetation types were evident, and the interannual fluctuation was weaker than that of the secondary forest. (2) The greenness of primary forest and secondary forest showed the same changing trend. It exhibited growth during leaf development in spring and attenuation during leaf fall in autumn. In the non-growing season, the degree of change in vegetation index of the primary forest was relatively stable and greater than that of the secondary forest, indicating that the understory of the secondary forest had high light transmittance. In vigorous growing season, the EVI and S2REP of the secondary forest were larger than those of the original forest, and the physiological activities of the vegetation canopy were more vigorous. Different satellite image data showed consistent performance, and the EVI peak of the secondary forest appeared slightly earlier than the original forest. (3) During the 35-year period from 1985 to 2019, the temperature in the study region had been on the rise, resulting in the increase in both vegetation greenness and the length of growing season; EVI of the primary forest was increasing, with the rate greater in summer than in other seasons. The interannual difference between spring and autumn for enhanced vegetation index was significant. (4) Compared with the primary forest, the interannual variation in EVI of the secondary forest was more correlated with spring temperature. At the beginning of growing season, both forests presented the same pattern that EVI and temperature were positively correlated. During the entire growing season, EVI increased steadily prior to the period when temperature reached a high level.
Conclusion Long-term continuous monitoring and analysis of canopy spectrum changes can effectively reflect the difference in vegetation phenology between the primary forest and the secondary forest. Temperature rise may be one of the important factors causing the greenness of the broadleaved Korean pine forest in Changbai Mountain of northeastern China.
- broadleaved Korean pine forest,
- Betula spp. & Populus spp. forest,
- canopy spectral reflectance,
- vegetation index,
- temperature

FullText(HTML)

References (44)

References

[1]	汲常萍. 长白山阔叶红松林和杨桦林不同土壤组分碳氮相关指标及差异研究[D]. 哈尔滨: 东北林业大学, 2014. Ji C P. Comparisons of soil carbon and nitrogen-related parameters in 5 different soil fractions between broad-leaved Korean Pine mixed forests and secondary poplar-birch forests in Changbai Mts[D]. Harbin: Northeast Forestry University, 2014.
[2]	徐振邦, 代力民, 陈吉泉, 等. 长白山红松阔叶混交林森林天然更新条件的研究[J]. 生态学报, 2001, 21(9):1413−1420. doi: 10.3321/j.issn:1000-0933.2001.09.003 Xu Z B, Dai L M, Chen J Q, et al. Natural regeneration condition in Pinus koraiensis broad-leaved mixed forest[J]. Acta Ecologica Sinica, 2001, 21(9): 1413−1420. doi: 10.3321/j.issn:1000-0933.2001.09.003
[3]	王树力, 武敬辉, 史永纯. 红松种群天然更新及幼年生长与林分结构关系的研究[J]. 吉林林学院学报, 1998, 14(8):6−10. Wang S L, Wu J H, Shi Y C. On the relationship between natural regeneration and early growth of Korean pine and stand structure[J]. Journal of Jilin Forestry University, 1998, 14(8): 6−10.
[4]	王连喜, 陈怀亮, 李琪, 等. 植物物候与气候研究进展[J]. 生态学报, 2010, 20(2):447−454. Wang L X, Chen H L, Li Q, et al. Research advances in plant phenology and climate[J]. Acta Ecologica Sinica, 2010, 20(2): 447−454.
[5]	Zhou G, Liu Q J, Xu Z Z, et al. How can the shade intolerant Korean pine survive under dense deciduous canopy?[J]. Forest Ecology and Management, 2020, 457: 117735. doi: 10.1016/j.foreco.2019.117735
[6]	许丹, 伍维模, 王家强, 等. 塔里木河流域上游天然胡杨叶片叶绿素与可见光−近红外光谱反射率的相关性研究[J]. 塔里木大学学报, 2012, 24(4):53−59. doi: 10.3969/j.issn.1009-0568.2012.04.0011 Xu D, Wu W M, Wang J Q, et al. Study on the correlation between leaf chlorophyll content of Populus euphratica and the hyperspectral remote sensing data in upstream of Tarim River[J]. Journal of Tarim University, 2012, 24(4): 53−59. doi: 10.3969/j.issn.1009-0568.2012.04.0011
[7]	赵恒谦, 张文博, 朱孝鑫, 等. 煤炭矿区植被冠层光谱土地复垦敏感性分析[J]. 光谱学与光谱分析, 2019, 39(6):1858−1863. Zhao H Q, Zhang W B, Zhu X X, et al. Analysis on susceptibility of vegetation canopy spectra in coal mining area to land reclamation[J]. Spectroscopy and Spectral Analysis, 2019, 39(6): 1858−1863.
[8]	罗娜娜, 赵文吉, 晏星. 在滞尘影响下的植被叶片光谱变化特征研究[J]. 光谱学与光谱分析, 2013, 33(10):2715−2720. doi: 10.3964/j.issn.1000-0593(2013)10-2715-06 Luo N N, Zhao W J, Yan X. Impact of dust-fall on spectral features of plant leaves[J]. Spectroscopy and Spectral Analysis, 2013, 33(10): 2715−2720. doi: 10.3964/j.issn.1000-0593(2013)10-2715-06
[9]	张雪红, 田庆久, 沈润平. 冬小麦冠层光谱的方向性特征分析[J]. 光谱学与光谱分析, 2010, 30(6):1600−1605. doi: 10.3964/j.issn.1000-0593(2010)06-1600-06 Zhang X H, Tian Q J, Shen R P. Analysis of directional characteristics of winter wheat canopy spectra[J]. Spectroscopy and Spectral Analysis, 2010, 30(6): 1600−1605. doi: 10.3964/j.issn.1000-0593(2010)06-1600-06
[10]	于泉洲, 周蕾, 王绍强, 等. 基于EO-1 Hyperion的中国典型森林冠层高光谱特征分析[J]. 云南大学学报(自然科学版), 2018, 40(5):947−954. Yu Q Z, Zhou L, Wang S Q, et al. An analysis on the spectrum characteristics of Chinese typical forest canopy in growing season based on EO-1 Hyperion images[J]. Journal of Yunnan University (Natural Sciences Edition), 2018, 40(5): 947−954.
[11]	马东辉, 柯长青. 南京冬季典型植被光谱特征分析[J]. 遥感技术与应用, 2016, 31(4):702−708. Ma D H, Ke C Q. Research on spectral characteristics of winter typical vegetation in Nanjing[J]. Remote Sensing Technology and Application, 2016, 31(4): 702−708.
[12]	项巧巧, 申广荣, 吴裕, 等. 上海典型植被夏季与冬季的光谱特征分析[J]. 上海交通大学学报(农业科学版), 2018, 36(5):14−21. Xiang Q Q, Shen G R, Wu Y, et al. Spectral characteristics of typical vegetation in Shanghai in both summer and winter[J]. Journal of Shanghai Jiaotong University (Agricultural Science), 2018, 36(5): 14−21.
[13]	程乾, 黄敬峰, 王人潮, 等. MODIS植被指数与水稻叶面积指数及叶片叶绿素含量相关性研究[J]. 应用生态学报, 2004, 15(8):1363−1367. doi: 10.3321/j.issn:1001-9332.2004.08.013 Cheng Q, Huang J F, Wang R C, et al. Correlation analysis of simulated MODIS vegetation indices and rice leaf area index and leaf chlorophyll content[J]. Chinese Journal of Applied Ecology, 2004, 15(8): 1363−1367. doi: 10.3321/j.issn:1001-9332.2004.08.013
[14]	Frampton W J, Dash J, Watmough G, et al. Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation[J]. Journal of Photogrammetry and Remote Sensing, 2013, 82: 83−92.
[15]	李明泽, 王雪, 高元科, 等. 大兴安岭植被指数年际变化及影响因子分析[J]. 北京林业大学学报, 2015, 37(5):1−10. Li M Z, Wang X, Gao Y K, et al. Inter-annual variation in vegetation index and analysis of factors affecting it in Daxing’anling Mountains[J]. Journal of Beijing Forestry University, 2015, 37(5): 1−10.
[16]	沈文娟, 李明诗. 基于长时间序列Landsat影像的南方人工林干扰与恢复制图分析[J]. 生态学报, 2017, 37(5):1438−1449. Shen W J, Li M S. Mapping disturbance and recovery of plantation forests in southern China using yearly Landsat time series observations[J]. Acta Ecologica Sinica, 2017, 37(5): 1438−1449.
[17]	Aulia M R, Liyantono, Setiawan Y, et al. Drought detection of west java’s paddy field using MODIS EVI satellite images (case study: Rancaekek and Rancaekek Wetan)[J]. Procedia Environmental Sciences, 2016, 33: 646−653. doi: 10.1016/j.proenv.2016.03.119
[18]	李明, 吴正方, 杜海波, 等. 基于遥感方法的长白山地区植被物候期变化趋势研究[J]. 地理科学, 2011, 31(10):1242−1248. Li M, Wu Z F, Du H B, et al. Growing-season trends determined from SPOT NDVI in Changbai Mountains, China, 1999−2008[J]. Scientia Geographica Sinica, 2011, 31(10): 1242−1248.
[19]	于健. 长白山阔叶红松林主要树种径向生长对气候变化响应及温度重建[D]. 北京: 北京林业大学, 2019. Yu J. Rosponse of radial growth of main tree species of broad-leaved Korean pine forest to climate change in Changbai Mountain and temperature reconstruction[D]. Beijing: Beijing Forestry University, 2019.
[20]	郝占庆, 代力民, 贺红士. 气候变暖对长白山主要树种的潜在影响[J]. 应用生态学报, 2001, 12(5):653−658. doi: 10.3321/j.issn:1001-9332.2001.05.003 Hao Z Q, Dai L M, He H S. Potential response of major tree species to climate warming in Changbai Mountain, Northeast China[J]. Chinese Journal of Applied Ecology, 2001, 12(5): 653−658. doi: 10.3321/j.issn:1001-9332.2001.05.003
[21]	周玉科, 刘建文. 基于MODIS NDVI和多方法的青藏高原植被物候时空特征分析[J]. 遥感技术与应用, 2018, 33(3):486−498. Zhou Y K, Liu J W. Spatio-temporal analysis of vegetation phenology with multiple methods over the Tibetan Plateau based on modis NDVI data[J]. Remote Sensing Technology and Application, 2018, 33(3): 486−498.
[22]	吴玉莲, 王襄平, 李巧燕, 等. 长白山阔叶红松林净初级生产力对气候变化的响应: 基于BIOME-BGC模型的分析[J]. 北京大学学报(自然科学版), 2014, 50(3):577−586. Wu Y L, Wang X P, Li Q Y, et al. Response of broad-leaved Korean pine forest productivity of Mt. Changbai to climate change: an analysis based on BIOME-BGC modeling[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2014, 50(3): 577−586.
[23]	徐琼瑶. 长白山植被景观变化遥感监测[D]. 北京: 北京林业大学, 2012. Xu Q Y. Monitoring vegetation water content with MODIS data in Changbai Mountain Area[D]. Beijing: Beijing Forestry University, 2012.
[24]	刘敏. 基于MODIS数据的长白山地区植被水分动态变化研究[D]. 吉林: 东北师范大学, 2010. Liu M. Monitoring vegetation water content with MODIS data in Changbai Mountain area[D]. Jilin: Northeast Normal University, 2010.
[25]	于泉洲, 王绍强, 黄昆, 等. 基于Hyperion高光谱数据的温带森林不同冠层结构的光谱特征分析[J]. 光谱学与光谱分析, 2015(7):1980−1985. doi: 10.3964/j.issn.1000-0593(2015)07-1980-06 Yu Q Z, Wang S Q, Huang K, et al. An analysis of the spectrums between different canopy structures based on Hyperion hyperspectral data in a temperate forest of northeast China[J]. Spectroscopy and Spectral Analysis, 2015(7): 1980−1985. doi: 10.3964/j.issn.1000-0593(2015)07-1980-06
[26]	姜超. 长白山五种槭属植物光合及光谱特性研究[D]. 北京: 北京林业大学, 2013. Jiang C. The photosynthetic and spectral reflectance characteristics of five Acer species in Changbai Mountain[D]. Beijing: Beijing Forestry University, 2013.
[27]	Gorelick N, Hancher M, Dixon M, et al. Google earth engine: planetary-scale geospatial analysis for everyone[J]. Remote Sensing of Environment, 2017, 202: 18−27. doi: 10.1016/j.rse.2017.06.031
[28]	杜文先. 长白山杨桦林物候多样性及叶面积指数季节动态[D]. 北京: 北京林业大学, 2018. Du W X. Phenological diversity and seasonal leaf area index pattern of Betula-Populus forest in Changbai Mountain[D]. Beijing: Beijing Forestry University, 2018.
[29]	于小舟, 袁凤辉, 王安志, 等. 积雪对长白山阔叶红松林土壤温度的影响[J]. 应用生态学报, 2010, 21(12):3015−3020. Yu X Z, Yuan F H, Wang A Z, et al. Effects of snow cover on soil temperature in broad-leaved Korean pine forest in Changbai Mountains[J]. Chinese Journal of Applied Ecology, 2010, 21(12): 3015−3020.
[30]	章钊华, 赵书河, 丛佃敏, 等. 基于遥感的泰山地区植被绿度趋势变化研究[J]. 地理空间信息, 2018, 16(7):65−68. doi: 10.3969/j.issn.1672-4623.2018.07.020 Zhang Z H, Zhao S H, Cong D M, et al. Vegetation greenness trend changes in Tai Mountain area based on remote sensing[J]. Geospatial Information, 2018, 16(7): 65−68. doi: 10.3969/j.issn.1672-4623.2018.07.020
[31]	梁守真, 马万栋, 王猛, 等. 冠层绿色FPAR与植被指数关系及其对气溶胶的敏感性分析[J]. 测绘与空间地理信息, 2018, 41(12):11−14. doi: 10.3969/j.issn.1672-5867.2018.12.004 Liang S Z, Ma W D, Wang M, et al. Analysis on the relationship between green FPAR and vegetation indices and their sensitivity to aerosol optical depth[J]. Geomatics & Spatial Information Technology, 2018, 41(12): 11−14. doi: 10.3969/j.issn.1672-5867.2018.12.004
[32]	王宗明, 国志兴, 宋开山, 等. 中国东北地区植被NDVI对气候变化的响应[J]. 生态学杂志, 2009, 28(6):1041−1048. Wang Z M, Guo Z X, Song K S, et al. Responses of vegetation NDVI in northeast China to climate change[J]. Chinese Journal of Ecology, 2009, 28(6): 1041−1048.
[33]	路中, 雷国平, 马泉来, 等. 基于重构的Landsat 8时间序列数据和温度植被指数的区域旱情监测[J]. 水土保持研究, 2018, 25(5):371−377. Lu Z, Lei G P, Ma Q L, et al. Regional drought monitoring based on reconstructed Landsat 8 data and temperature vegetation index[J]. Research of Soil and Water Conservation, 2018, 25(5): 371−377.
[34]	张墨谦. 遥感时间序列数据的特征挖掘: 在生态学中的应用[D]. 上海: 复旦大学, 2014. Zhang M Q. Characterizing the remotely sensed time series data for ecological applications[D]. Shanghai: Fudan University, 2014.
[35]	李百超. 基于高光谱成像技术的苹果叶片氮素含量估测研究[D]. 泰安: 山东农业大学, 2018. Li B C. Estimation of nitrogen content in apple leaves based on hyperspectral imaging technology[D]. Taian: Shandong Agricultural University, 2018.
[36]	李淑敏, 李红, 孙丹峰, 等. PROSAIL冠层光谱模型遥感反演区域叶面积指数[J]. 光谱学与光谱分析, 2009, 29(10):2725−2729. doi: 10.3964/j.issn.1000-0593(2009)10-2725-05 Li S M, Li H, Sun D F, et al. Estimation of regional leaf area index by remote sensing inversion of PROSAIL canopy spectral model[J]. Spectroscopy and Spectral Analysis, 2009, 29(10): 2725−2729. doi: 10.3964/j.issn.1000-0593(2009)10-2725-05
[37]	张朋涛. 青海湖流域植被叶绿素含量遥感定量反演研究[D]. 西宁: 青海师范大学, 2015. Zhang P T. Chlorophyll content of vegetation in Qinghai Lake Basin quantitative remote sensing inversion[D]. Xining: Qinghai Normal University, 2015.
[38]	徐光彩, 庞勇, 李增元, 等. 小兴安岭主要树种冠层光谱季相变化研究[J]. 光谱学与光谱分析, 2013, 33(12):3303−3307. doi: 10.3964/j.issn.1000-0593(2013)12-3303-05 Xu G C, Pang Y, Li Z Y, et al. The changes of forest canopy spectral reflectance with seasons in Xiaoxing’anling[J]. Spectroscopy and Spectral Analysis, 2013, 33(12): 3303−3307. doi: 10.3964/j.issn.1000-0593(2013)12-3303-05
[39]	周玉科. 基于遥感的中国东北植被物候不对称特征分析[J]. 遥感技术与应用, 2019, 34(2):345−354. Zhou Y K. Depicting the asymmetries of vegetation phenology over northeast China using remote sensing NDVI dataset[J]. Remote Sensing Technology and Application, 2019, 34(2): 345−354.
[40]	赵冰茹, 马龙. 基于MODIS EVI的内蒙古草地多源信息综合分类研究[J]. 浙江大学学报(农业与生命科学版), 2007, 33(3):342−347. Zhao B R, Ma L. Multi-source data complex classification of grassland in Inner Mongolia based on MODIS EVI[J]. Journal of Zhejiang University (Agriculture & Life Sciences), 2007, 33(3): 342−347.
[41]	方灿莹, 王琳, 徐涵秋. 不同植被红边指数在城市草地健康判别中的对比研究[J]. 地球信息科学学报, 2017, 19(10):1382−1392. Fang C Y, Wang L, Xu H Q. A comparative study of different red edge indices for remote sensing detection of urban grassland health status[J]. Journal of Geo-Information Science, 2017, 19(10): 1382−1392.
[42]	赵冰茹, 贾翔, 金慧, 等. 1958—2015年长白山气候变化特征分析[J]. 北华大学学报(自然科学版), 2017, 18(6):727−731. Zhao B R, Jia X, Jin H, et al. Characteristics of climate change in Changbai Mountain from 1958 to 2015[J]. Journal of Beihua University (Natural Science), 2017, 18(6): 727−731.
[43]	王小霞, 刘志华, 焦珂伟. 2000—2017年东北森林NDVI时空动态及其驱动因子[J]. 生态学杂志, 2020, 39(9):2878−2886. Wang X X, Liu Z H, Jiao K W. Spatiotemporal dynamics of normalized difference vegetation index (NDVI) and its drivers in forested region of Northeast China during 2000−2017[J]. Chinese Journal of Ecology, 2020, 39(9): 2878−2886.
[44]	史国强, 牛丽君, 郭艳双,等. 长白山北坡春季物候特征及其对气候变化的响应[J]. 东北师大学报(自然科学版), 2019, 51(4):124−130. Shi G Q, Niu L J, Guo Y S, et al. Phenological characteristics of spring on the north slope of Changbai Mountain and its response to climate change[J]. Journal of Northeast Normal University (Natural Science Edition), 2019, 51(4): 124−130.

[1]	Zhang Houjiang, Li Yufeng. Content and research progress in nondestructive testing of wooden structures in ancient architecture[J]. Journal of Beijing Forestry University, 2024, 46(4): 1-13. DOI: 10.12171/j.1000-1522.20240015
[2]	Ye Qi, Guan Cheng, Zhang Houjiang, Gong Yingchun, Sui Yongfeng, Liu Lige. Optimization of finger joint parameters and nondestructive testing of bending properties of radiata pine laminates[J]. Journal of Beijing Forestry University, 2022, 44(3): 148-160. DOI: 10.12171/j.1000-1522.20210351
[3]	Li Huan, Guan Cheng, Zhang Houjiang, Liu Jinhao, Zhou Jianhui, Xin Zhenbo. Determining modulus of elasticity of full-size plywood panel simply supported on two opposite sides using a vibration method[J]. Journal of Beijing Forestry University, 2021, 43(2): 138-149. DOI: 10.12171/j.1000-1522.20200300
[4]	WANG Yun-lu, WANG Zheng, LI Min-min, CAO Yu. Discussion on static testing method of material MDF constants of elastic modulus, Poisson's ratio and shear modulus[J]. Journal of Beijing Forestry University, 2017, 39(10): 117-121. DOI: 10.13332/j.1000-1522.20170107
[5]	FENG Li, QIN Nan. Dominant factor analysis of dynamic Young modulus of poplar LVL.[J]. Journal of Beijing Forestry University, 2012, 34(4): 146-148.
[6]	ZHANG Hou-jiang, GUO Zhi-ren, John F Hunt, FU Feng. Measuring modulus of elasticity for thin wood composites using a dynamic method[J]. Journal of Beijing Forestry University, 2010, 32(2): 149-152.
[7]	ZHAN Jianfeng, GU Jiyou, CAI Yingchun.. Dynamic viscoelastic characteristics of larch timber during conventional drying process. [J]. Journal of Beijing Forestry University, 2009, 31(1): 125-129.
[8]	JIANG Jiali, LV Jian-xiong.. Dynamic viscoelastic properties of drying treated wood.[J]. Journal of Beijing Forestry University, 2008, 30(3): 96-100.
[9]	JIANG Jia-li, LÜ Jian-xiong, ZHAO Guang-jie. Viscoelastic properties of wood treated by different reagents[J]. Journal of Beijing Forestry University, 2006, 28(1): 88-92.
[10]	ZHANG Hou-jiang, SHEN Shi-jie, CUI Ying-ying, MIAO Yi, WANG Ying-kun. Measuring elastic modulus of wood using vibration method[J]. Journal of Beijing Forestry University, 2005, 27(6): 91-94.

Cited By

Cited by

Periodical cited type(9)

1.	辛守英，王晓红，焦琳琳. 基于遥感数据和优化Blending算法的人工林地上生物量估算研究. 西北林学院学报. 2025(02): 207-219 .
2.	郝君，吕康婷，胡天祺，王云阁，徐刚. 基于机器学习的红树林生物量遥感反演研究. 林草资源研究. 2024(01): 65-72 .
3.	李敏，陈利，李静泰，闫丹丹，刘垚，吴翠玲，栾兆擎. 基于Sentinel-2数据的互花米草地上生物量反演. 海洋环境科学. 2024(03): 386-397 .
4.	廖易，张加龙，鲍瑞，许冬凡. 基于Landsat的高山松地上生物量动态变化估测模型构建. 西南林业大学学报(自然科学). 2023(01): 117-125 .
5.	郭芮，伏帅，侯蒙京，刘洁，苗春丽，孟新月，冯琦胜，贺金生，钱大文，梁天刚. 基于Sentinel-2数据的青海门源县天然草地生物量遥感反演研究. 草业学报. 2023(04): 15-29 .
6.	廖易，张加龙，鲍瑞，许冬凡，王书贤，韩冬阳. 引入地形因子的高山松地上生物量动态估测. 生态学杂志. 2023(05): 1243-1252 .
7.	王熙媛，张王菲，李云，杨仙保. 依据光学遥感特征优选的森林地上生物量反演. 东北林业大学学报. 2022(04): 47-54 .
8.	陈园园，张晓丽，高显连，高金萍. 基于Sentinel-1和Sentinel-2A的西小山林场平均树高估测. 应用生态学报. 2021(08): 2839-2846 .
9.	陈小芳，李军，李新伟，周毅. 基于高光谱的水稻生物量估测模型研究. 安徽科技学院学报. 2021(05): 53-59 .

Other cited types(11)

Get Citation

PDF

XML

Article views (1333) PDF downloads (123) Cited by(20)

Turn off MathJax

Article Contents

Abstract

References

Canopy spectral characteristics of broadleaved Korean pine forest in different successional stages and its relation with temperature in Changbai Mountain of northeastern China