Above-Ground Biomass Prediction of Arbor Forest in Altay Mountain Area Based on High-Resolution Remote Sensing Data

doi:10.13466/j.cnki.lyzygl.2023.02.014

Abstract

Abstract:

In order to accurately and conveniently estimate forest biomass at the regional scale,remote sensing characteristic variables such as vegetation index and texture were extracted based on high-resolution remote sensing data and field survey data,and the nearest neighbor algorithm (k-NN) was used to construct a forest aboveground biomass prediction model.The results showed that using k-NN to quantitatively estimate the biomass of tree forests at the regional scale,when k value was 2 and the characteristics were B1 (band 1),SR (simple vegetation index),NDVI (normalized vegetation index) and B4 (band 4),the forest biomass estimation results were optimal.The above ground biomass was 8 039 000 tons,and the average biomass per unit area was 82.15 t/hm².When the main age group of arbor forest wasmature forest,its area and biomass ratio werethe highest.The unit biomass of arbor forest was higher in the altitude range of 1 500 ~ 2 400m.

Key words: arbor forest, above-ground biomass, nearest neighbor algorithm (k-NN), remote sensing inversion, feature selection

CLC Number:

ZHANG Huifang, ZHU Yali, ZHANG Jinglu, GAO Jian, DILIXIATI·Baoerhan . Above-Ground Biomass Prediction of Arbor Forest in Altay Mountain Area Based on High-Resolution Remote Sensing Data[J]. FOREST RESOURCES WANAGEMENT, 2023, 0(2): 104-110.

Figures/Tables 12

Fig.1

Tab.1

Tab.2

Equations for 4vegetation indexes

植被指数	公式
归一化植被指数(NDVI)	NDVI= $N I R - R e d N I R + R e d$
简单植被指数(SR)	SR= $N I R R e d$
大气阻抗植被指数(ARVI)	ARVI= $N I R - R e d - r B l u e - R e d N I R + R e d - r B l u e - R e d$
增强型植被指数(EVI)	EVI=2.5× $N I R - R e d N I R + 6 × R e d - 7.5 × B l u e + 1$

Tab.2

Tab.3

Texture calculation method based on gray level co-occurrence matrix

纹理	公式
方差	V= $∑ i, j = 0 N - 1$ i×P_i_,_j(i-Me)²
协同性	H= $∑ i, j = 0 N - 1$ i× $P i, j 1 + (i - j) 2$
对比度	Co= $∑ i, j = 0 N - 1$ i×P_i,j(i-j)²
相异性	D= $∑ i, j = 0 N - 1$ i×P_i,j $i - j$
熵	E= $∑ i, j = 0 N - 1$ i×P_i,j(-lnP_i,j)
二阶矩	S= $∑ i, j = 0 N - 1$ i× $P i, j 2$
相关性	Cr= $∑ i, j = 0 N - 1$ i×P_i,j $(i - M e) (j - M e) V a i × V a j$

Tab.3

Tab.4

Precision index formula and description

精度指标	公式
确定系数(R²)	R²= $∑ i = 1 n (y i - y -) 2 - ∑ i = 1 n (y i - y^i) 2 ∑ i = 1 n (y i - y -) 2$
估计值的标准误差(SEE)	SEE= $∑ i = 1 n (y i - y^i) 2 / (n - p)$
平均系统误差(MSE)	MSE= $1 n$ ∑(y_i- $y^i$ )/ $y^i$ ×100%
平均预估误差(MPE)	MPE=t_a× $S E E y - i$ / $n$ ×100%
模型精度(P)	P=(1-MPE)×100%

Tab.4

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Tab.5

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样地数/块	单位面积生物量/(t/hm²)
样地数/块	最大值	最小值	平均值	中位数	标准差
51	329.23	0.505	127.37	97.63	88.26

龄组	面积占比/%	单位面积平均生物量/hm²	总生物量/t	生物量占比/%
幼龄林	0.68	85.57	52253.66	0.65
中龄林	5.42	86.72	422852.69	5.26
近熟林	19.66	87.45	1546708.33	19.24
成熟林	61.47	89.06	4924706.46	61.26
过熟林	12.77	95.07	1092503.44	13.59
总计			8039024.585