Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong

doi:10.12302/j.issn.1000-2006.202202015

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (1): 169-178.doi: 10.12302/j.issn.1000-2006.202202015

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Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong

ZHOU Youfeng¹(), XIE Binglou²^,^*(), LI Mingshi¹

1. College of Forestry and Grassland, College of Soil and Water Conservation, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
2. Forest Resources Monitoring Center of Zhejiang Prorince, Hangzhou 310020, China

Received:2022-02-18 Revised:2022-04-07 Online:2024-01-30 Published:2024-01-24
Contact: XIE Binglou E-mail:281833932@qq.com;xiebinglou2022@163.com

Abstract

Abstract:

【Objective】 Forest aboveground biomass (AGB) is an important indicator for evaluating forest ecosystem health status and carbon sink potential. Accurate and quick mapping regional forest AGB has become intensively researched in forest ecosystem status assessment and global climate change studies in recent years. The major objective of this study was to develop a framework for improving the mapping accuracy of AGB in a subtropical forested area with complex terrain. 【Method】 Spectral features, textural indices, backscattering coefficients, and topographical variables were derived from Landsat 5 TM, ALOS-1 PALSAR-1 data and STRM DEM. Next, in tandem with national forest inventory plot measurements, a random forest/Co-Kriging framework that combines the advantages of random forest (RF) and a geostatistical approach was proposed to map AGB in northern Guangdong Province. 【Result】 The experimental results showed that the ordinary Kriging (OK) and Co-Kriging (CK) were able to predict the distribution of the RF-predicted AGB residuals. The predicted structured components of the residuals adding onto the RF predictions could improve the mapping accuracy of AGB to some extent. After the validation of the independent 20% dataset, the determination coefficient between the predictions and the observations increased from 0.46 (RF) to 0.51 (RFOK) and to 0.57 (RFCK). The root mean square error decreased from 32.48 to 31.58 and to 29.80 t/hm² accordingly. The mean absolute error decreased from 27.28 to 26.63 and to 25.12 t/hm². Overall, co-Kriging, which considers elevation as a co-variable, was better than ordinary Kriging in predicting AGB residuals. 【Conclusion】 The RFCK framework provides an accurate and reliable method to map subtropical AGB with complex topography. The resulting AGB maps contribute to targeted forest resource management and promote forest carbon sequestration and sustainable forest management under global warming scenarios.

Key words: forest aboveground biomass, random forest, Co-Kriging, ALOS-1 PALSAR-1, Landsat 5 TM, national forest inventory, north Guangdong Province

CLC Number:

S771.8

ZHOU Youfeng, XIE Binglou, LI Mingshi. Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(1): 169-178.

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树种 species	异速生长方程 allometric growth equations	树种 species	异速生长方程 allometric growth equations
杉木 Cunninghamia lanceolata	W_干=0.340 15 D^{-0.392 39}H^{0.408 90}V	马尾松 Pinus massoniana	W_干=0.292 89 D^{0.146 21}H^{0.008 924}V
	W_枝=0.271 40 D^{1.072 61}H^{0.281 71}V		W_枝=0.125 32 V
	W_叶=0.464 93 D^{-0.328 92}H^{0.046 374}V		W_叶=0.079 612 D^{-0.352 63}H^{0.015 724}V
阔叶树 broad-leaved tree	W_干=0.297 00 D^{0.212 72}H^{0.046 374}V	桉树 Eucalyptus robusta	W_干=0.237 19 D^{0.315 57}H^{-0.022 517}V
	W_枝=0.545 41 D^{0.274 01}H^{-0.165 65}V		W_枝=0.090 123 D^{-0.302 67}H^{0.019 109}V
	W_叶=0.225 26 D^{-0.388 74}H^{-0.219 25}V		W_叶=0.052 063 7 D^{-0.216 66}H^{0.014 372}V
毛竹 Phyllostachys edulis	W_干=0.000 096 7 D^{0.217 5}N	杂竹 miscellaneous bamboo	W_干=0.001 Ne^{3.274 82-9.674 4/}^D
	W_枝=0.000 831 98 D^{1.177 4}N^0.648		W_枝=0.001 N/(0.685+12.893e^-^D)
	W_叶=0.000 509 9 D^{1.177 4}N^0.648		W_叶=0.001 N/(1.056+48.560 9e^D)

林分类型 stand type	样地数量 sample size	平均胸径/cm mean DBH	平均树高/m mean tree height	森林地上生物量AGB/(t·hm^-2)
林分类型 stand type	样地数量 sample size	平均胸径/cm mean DBH	平均树高/m mean tree height	最小值 min	最大值 max	平均值 mean	标准差 SD
阔叶纯林pure broad-leaved forest	39	10.0	12	2.97	168.70	38.51	33.73
针叶纯林pure coniferous forest	43	10.6	10	6.48	87.78	27.05	23.65
阔叶混交林mixed broad-leaved forest	97	12.0	10	2.83	203.16	79.36	49.99
针叶混交林mixed coniferous forest	9	13.7	10	11.76	131.42	55.43	40.53
针阔混交林mixed broadleaf-conifer forest	17	12.5	10	13.78	91.15	49.92	24.02
竹林bamboo forest	26	6.6	9	2.08	202.50	60.10	60.10
灌木林shrubwood	14	8.9	7	12.37	158.53	67.26	58.22
总计total	245	9.2	11

克里金法 Kriging	变异函数模型 variogram models	块金值 nugget	基台值 sill	块金值/基台值 nugget/sill	变程/km range	R²	残差平方和 RSS
	指数函数exponential	376.32	387.94	0.97	72.32	0.153	8 921.4
OK	球面函数spherical	400.50	407.20	0.98	78.21	0.161	8 784.6
	高斯函数Gaussian	372.30	387.03	0.96	72.70	0.162	8 754.5
	指数函数exponential	390.86	411.43	0.95	61.34	0.188	8 287.4
CK	球面函数spherical	365.24	376.54	0.97	50.46	0.192	8 234.6
	高斯函数Gaussian	372.37	393.68	0.95	48.47	0.194	8 145.2

Mapping regional forest aboveground biomass from random forest Co-Kriging approach: a case study from north Guangdong

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