Analysis of spatial and temporal variations of aboveground biomass and the factors affecting it in a typical forest area in the central Tianshan Mountains

doi:10.6048/j.issn.1001-4330.2024.09.019

Abstract

Abstract:

【Objective】 aboveground biomass at different time and spatial scales and analyze its influencing factors, this project aims to reveal the dynamic change process of the ecosystem in the natural forest area and explore the environmental factors affecting the change of aboveground biomass. 【Methods】 Based on the remote sensing data of the past 20 years from 2000 to 2022 and the actual sample data of the forest land in the study area, this study used the remote sensing information to establish an estimation model, estimate the aboveground biomass in the typical natural forest area of Tianshan Mountain, Xinjiang, and analyze the temporal and spatial dynamic changes of biomass in the area and the influencing factors of the aboveground biomass changes of natural forest lands. 【Results】 The spatial distribution of aboveground biomass in woodland in the study area was obvious, with high biomass in northeast China and low biomass in southwest China, mainly concentrated in the northern part of the study area. In 2022, the forest biomass of the Internship Forest Farm was about 3.728×10⁶ t, the maximum value was about 559.67 t/hm², and the average biomass was about 233.45 t/hm². The biomass in the northeast was higher than in the southwest, and the increase in latitude led to a decrease in biomass. There were differences in the interannual variation trend of aboveground biomass in woodland in the study area, and the overall state was stable and growing, with 52.63% of the area biomass increasing and 47.37% of the area biomass decreasing. The biomass per unit area of different forest age stages increased with the increase of forest age, and the biomass of the tree layer was the highest in mature forests. 【Conclusion】 In the forest land in the study area, the area and biomass proportion of juvenile forest to overmature forest are different, and it is necessary to increase the investment in natural regeneration and artificial tending work. From 2000 to 2022, the aboveground biomass of forest land in the study area gradually increased from north to south, and precipitation played a positive role in biomass growth. In the southwest area of the Internship Forest Farm, human activities are frequent, and changes in temperature and precipitation affect the growth of trees and grasslands, resulting in the high distribution of biomass in the northeast and low distribution of biomass in the southwest.

Key words: natural forest; biomass; remote sensing information model; spatial distribution

摘要：

【目的】研究天山中部典型林区地上生物量在不同时间和空间尺度上的变化规律,分析其影响因素和天然林区生态系统的动态变化过程,探讨影响地上生物量变化的环境因素。【方法】以2000~2022年近20年的遥感数据和研究区林地实际样地数据为基础,利用遥感信息建立估测模型,估算新疆天山典型天然林区地上生物量,分析该地区生物量的时空动态变化,分析天然林地地上生物量变化的影响因素。【结果】研究区林地地上生物量空间分布差异明显,东北地区生物量高,西南地区生物量低,主要集中在研究区北部。2022年实习林场森林生物量约为3.728×10⁶ t,最大值约为559.67 t/hm²,平均生物量约为233.45 t/hm²。东北地区生物量高于西南地区,纬度增加导致生物量减少。研究区林地地上生物量年际变化趋势存在差异,总体呈稳定和增长状态,52.63%的面积生物量增加,47.37%的面积生物量减小。不同林龄阶段的单位面积生物量随林龄增加而增加,乔木层生物量在成熟林时最高。【结论】幼龄林至过熟林面积和生物量比重不同,需要加大天然更新和人工抚育工作投入。2000~2022年研究区林地地上生物量总体由北向南逐渐增加,降水对生物量增长起到重要作用。实习林场西南区域人类活动频繁,气温和降水变化影响树木和草地生长,导致东北高、西南低的生物量分布。

关键词: 天然林, 生物量, 遥感信息模型, 空间分布

CLC Number:

S717

XIAO Shuting, YAN An, WANG Weixia, ZHANG Qingqing, HOU Zhengqing, MA Mengqian, SUN Zhe. Analysis of spatial and temporal variations of aboveground biomass and the factors affecting it in a typical forest area in the central Tianshan Mountains[J]. Xinjiang Agricultural Sciences, 2024, 61(9): 2237-2244.

肖淑婷, 颜安, 王卫霞, 张青青, 侯正清, 马梦倩, 孙哲. 天山中部典型林区地上生物量时空变化及影响因素分析[J]. 新疆农业科学, 2024, 61(9): 2237-2244.

Figures/Tables 10

Tab.1 Model building methods

模型 Model	表达式 Expression
岭回归模型 Ridge regression model
最小二乘法模型 Least squares model	$Y = β_{0} + β_{1} X_{0} + β_{2} X_{1} + \dots + β_{i} X_{i} .$
逐步回归模型 Stepwise regression model

Tab.1 Model building methods

模型 Model	表达式 Expression
岭回归模型 Ridge regression model
最小二乘法模型 Least squares model	$Y = β_{0} + β_{1} X_{0} + β_{2} X_{1} + \dots + β_{i} X_{i} .$
逐步回归模型 Stepwise regression model

Tab.2 Accuracy index

精度指标 Precision index	公式 Formula
决定系数(R²) Coefficient of determination	$R^{2} = \frac{\overset{n}{\sum_{i = 1}} ({\overset{\land}{Y}}_{i} - y_{i})^{2}}{\overset{n}{\sum_{i = 1}} (Y_{i} - y_{i})^{2}} .$
均方根误差(RMSE) Root mean square error	$R M S E = \sqrt{\frac{\overset{n}{\sum_{i = 1}} (Y_{i} - {\overset{\land}{Y}}_{i})^{2}}{n}} .$
模型精度(P) Model accuracy	$P = (1 - \frac{\sqrt{\overset{n}{\sum_{i = 1}} {(Y - {\overset{\land}{Y}}_{i})}^{2}}}{n}) \times 100 .$

Tab.2 Accuracy index

精度指标 Precision index	公式 Formula
决定系数(R²) Coefficient of determination	$R^{2} = \frac{\overset{n}{\sum_{i = 1}} ({\overset{\land}{Y}}_{i} - y_{i})^{2}}{\overset{n}{\sum_{i = 1}} (Y_{i} - y_{i})^{2}} .$
均方根误差(RMSE) Root mean square error	$R M S E = \sqrt{\frac{\overset{n}{\sum_{i = 1}} (Y_{i} - {\overset{\land}{Y}}_{i})^{2}}{n}} .$
模型精度(P) Model accuracy	$P = (1 - \frac{\sqrt{\overset{n}{\sum_{i = 1}} {(Y - {\overset{\land}{Y}}_{i})}^{2}}}{n}) \times 100 .$

Fig.1 Person correlation analysis of characteristic variables

Tab.3 Formula for calculating vegetation index

	名称 Name	VI
NDVI	归一化植被指数	$N D V I = \frac{N I R - R E D}{N I R + R E D} .$
NDMI	归一化差值含水指数	$N D M I = \frac{N I R - S W I R 1}{N I R + S W I R 1} .$
EVI	增强型植被指数	$\begin{array}{l} E V I = 2.5 \times \\ [\frac{N I R - R E D}{(N I R + 6 \times R E D - 7.5 \times B L U E + 1)}] . \end{array}$
DVI	差值植被指数	$D V I = N I R - R E D$
SAVI	土壤调节植被指数	$S A V I = 1.5 \times [\frac{N I R - R E D}{(N I R + R E D + 0.5)}] .$
OSAVI	优化型土壤调节植被指数	$O S A V I = \frac{N I R - R E D}{(N I R + R E D + 0.16)} .$
SR	比植被指数	$S R = \frac{N I R}{R E D} .$
RDVI	重归一化植被指数	$R D V I = \frac{N I R - R E D}{\sqrt{(N I R + R E D)} .}$
PVI	垂直植被指数	$P V I = \frac{(N I R - a \times R E D - b)}{\sqrt{(1 + a^{2})}} .$ $a = 0.3, b = 0.5$

Tab.3 Formula for calculating vegetation index

	名称 Name	VI
NDVI	归一化植被指数	$N D V I = \frac{N I R - R E D}{N I R + R E D} .$
NDMI	归一化差值含水指数	$N D M I = \frac{N I R - S W I R 1}{N I R + S W I R 1} .$
EVI	增强型植被指数	$\begin{array}{l} E V I = 2.5 \times \\ [\frac{N I R - R E D}{(N I R + 6 \times R E D - 7.5 \times B L U E + 1)}] . \end{array}$
DVI	差值植被指数	$D V I = N I R - R E D$
SAVI	土壤调节植被指数	$S A V I = 1.5 \times [\frac{N I R - R E D}{(N I R + R E D + 0.5)}] .$
OSAVI	优化型土壤调节植被指数	$O S A V I = \frac{N I R - R E D}{(N I R + R E D + 0.16)} .$
SR	比植被指数	$S R = \frac{N I R}{R E D} .$
RDVI	重归一化植被指数	$R D V I = \frac{N I R - R E D}{\sqrt{(N I R + R E D)} .}$
PVI	垂直植被指数	$P V I = \frac{(N I R - a \times R E D - b)}{\sqrt{(1 + a^{2})}} .$ $a = 0.3, b = 0.5$

Fig.2 Scatterplot comparing the measured and predicted values of each model

Tab.4 Accuracy evaluation table of aboveground biomass model

模型名称 Model	评价指标Evaluation index
模型名称 Model	R²	RMSE	P(%)
岭回归模型 Ridge regression model	0.729	0.242	94.95
最小二乘法模型 Least squares model	0.75	0.233	95.15
逐步回归模型 Stepwise regression model	0.693	0.258	94.63

Fig.3 Average aboveground biomass of forest land from 2000 to 2022 in the study area (a), and the trend of aboveground biomass in the study area from 2000 to 2022 (b)

Fig.4 Distribution of aboveground biomass at different forest ages

Fig.5 Interannual variation of aboveground biomass in forest land in the study area, 2000-2022

Fig.6 Relationship between aboveground biomass, precipitation(a) and temperature(b) in the study area

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