欢迎访问林草资源研究

Forest and Grassland Resources Research >

2021 , Vol. 0 >Issue 6: 83 - 89

DOI: https://doi.org/10.13466/j.cnki.lyzygl.2021.06.014

Research on Estimation of Aboveground Biomass and Scale Conversion for Pinus densata Mast

  • Jinhao TANG ,
  • Jialong ZHANG ,
  • Liye CHEN ,
  • Tao CHENG
Expand
  • 1. Faculty of Forestry,Southwest Forestry University,Kunming 650224,China
    2. Zhijiang Dong Autonomous County Forestry Bureau,Huaihua,Hunan 419100,China
    3. National Geomatics Center of China,Beijing 100830,China

Received date: 2021-09-02

  Revised date: 2021-10-28

  Online published: 2022-01-12

Fold

Abstract

In order to study the impact of scaling up of remote sensing images for forest biomass estimation,this paperused the Nearest Neighbor method,Bilinear Interpolation,Cubic Convolution Interpolation,Local Average method and Pixel Aggregation method to convert original Landsat-8,Sentinel-2A and Spot-7 images to low spatial resolution images in Shangri-La City. Then,this paper combined the aboveground biomass data of Pinus densata Mast. obtained from field surveys and the real image of target scale to establish the biomass estimation models of Random Forest (RF) and Gradient Boosted Regression Trees (GBRT) respectively,and compared the modeling effects of the two models. The results showed that: after being scaled up by the Nearest Neighbor method,the estimation accuracy (P) of RF and GBRT modeling based on Spot-7 images were 76.65% and 75.55%,respectively.,the P values of the two models based on Sentinel-2 images were 81.78% and 72.74% respectively,which were better than the other four scale conversion methods;The estimation accuracy of the RF estimation (81.78%~63.94%) of the image constructed by the five scale conversion methods was better than the estimation accuracy of GBRT (75.55%~61.03%). Sentinel-2A images were more suitable for scaling up to estimate forest biomass. The study results can provide reference for the selection of scale conversion methods and biomass estimation models.

Key words： multisource remote sensing data; aboveground biomass; scale conversion; random forests; gradient boosting regression tree

Cite this article

Jinhao TANG , Jialong ZHANG , Liye CHEN , Tao CHENG . Research on Estimation of Aboveground Biomass and Scale Conversion for Pinus densata Mast[J]. Forest and Grassland Resources Research, 2021 , 0(6) : 83 -89 . DOI: 10.13466/j.cnki.lyzygl.2021.06.014

References

[1] 刘茜, 杨乐, 柳钦火, 等. 森林地上生物量遥感反演方法综述[J]. 遥感学报, 2015, 19(1):62-74.
[2] 吴田军, 骆剑承, 沈占锋, 等. 高分辨率遥感土地覆盖分类技术的应用研究——以重庆市黔江贫困区为例[J]. 地球信息科学学报, 2016, 18(3):353-361.
[3] 赵鑫. 基于机载激雷达的森林地上生物量估测研究[D]. 西安:西安科技大学, 2020.
[4] 许振宇, 李盈昌, 李明阳, 等. 基于Sentinel-1A和Landsat 8数据的区域森林生物量反演[J]. 中南林业科技大学学报, 2020, 40(11):147-155.
[5] 张加龙, 胥辉, 陆驰. 应用Landsat8 OLI和GBRT对高山松地上生物量的估测[J]. 东北林业大学学报, 2018, 46(8):25-30.
[6] 陆驰, 张加龙, 王爱芸, 等. 基于森林小班的香格里拉市高山松生物量遥感建模[J]. 西南林业大学学报:自然科学, 2017, 37(3):152-158.
[7] 李明泽. 东北林区森林生物量遥感估算及分析[D]. 哈尔滨:东北林业大学, 2010.
[8] Foody G M., Cutler M E, Mcmorrow, et al. Mapping the biomass of Bornean tropical rain forest from remotely sensed data[J]. Global Ecology & Biogeography, 2001, 10(4):379-387.
[9] 胡凯龙, 刘清旺, 李世明, 等. 运用融合纹理和机载LiDAR特征模型估测森林地上生物量[J]. 东北林业大学学报, 2018, 46(1):52-57.
[10] Persson H J, Jonzén J, Nilsson M. Combining TanDEM-X and Sentinel-2 for large-area species-wise prediction of forest biomass and volume[J/OL]. https://doi.org/10.1016/j.jag.2020.102275 .
[11] 徐凯健, 田庆久, 朱小波, 等. 空间尺度上推转换对影像信息的影响分析[J]. 测绘科学, 2019, 44(5):117-125.
[12] 李小文, 王祎婷. 定量遥感尺度效应刍议[J]. 地理学报, 2013, 68(9):1163-1169.
[13] 吴田军, 骆剑承, 沈占锋, 等. 高分辨率遥感土地覆盖分类技术的应用研究——以重庆市黔江贫困区为例[J]. 地球信息科学学报, 2016, 18(3):353-361.
[14] 栾海军, 田庆久, 余涛, 等. 定量遥感升尺度转换研究综述[J]. 地球科学进展, 2013, 28(6):657-664.
[15] 陆驰, 张加龙, 王爱芸, 等. 基于Landsat TM的香格里拉市高山松生物量估测重建[J]. 林业调查规划, 2016, 41(6):1-7.
[16] 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.
[17] 孙雪莲. 基于Landsat8-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.
[18] 张毅, 陈成忠, 吴桂平, 等. 遥感影像空间分辨率变化对湖泊水体提取精度的影响[J]. 湖泊科学, 2015, 27(2):335-342.
[19] 全斌, 刘二洋. 金字塔影像结构在影像匹配中的应用[J]. 测绘通报, 2010, 396(3):10-12.
[20] 姚远, 陈曦, 钱静. 定量遥感尺度转换方法研究进展[J]. 地理科学, 2019, 39(3):367-376.
[21] 周觅, 张杰林. 遥感影像尺度转换及最优尺度选择探讨[J]. 世界核地质科学, 2011, 28(2):94-98.
[22] Breiman L. Random Forests[J]. Machine Learning, 2001, 45(1):5-32.
[23] 王森森. 基于图像样例的人脸检测算法研究[D]. 南昌:南昌大学, 2016.
[24] Friedman J H. Greedy Function Approximation:a Gradient Boosting Machine[J]. The Annals of Statistics, 2001, 29(5):1189-1232.
[25] Tian Yuhong, Wang Yujie, Zhang Yu, et al. Radiative Transfer Based Scaling of Lai Retrievals From Reflectance Data of Different Resolutions[J]. Remote Sensing of Environment, 2003, 84(1):143-159.
[26] 田庆久, 金震宇. 森林叶面积指数遥感反演与空间尺度转换研究[J]. 遥感信息, 2006(4):5-11.
[27] 范闻捷, 盖颖颖, 徐希孺, 等. 遥感反演离散植被有效叶面积指数的空间尺度效应[J]. 中国科学:地球科学, 2013, 43(2):280-286.
[28] 吴迪, 范文义. 激光雷达协同多角度光学遥感数据反演树高[J]. 北京林业大学学报, 2014, 36(4):8-15.
[29] 孙雪莲, 舒清态, 欧光龙, 等. 基于随机森林回归模型的思茅松人工林生物量遥感估测[J]. 林业资源管理, 2015(1):71-76.
Options
Outlines

/

〈 〉