基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算

doi:10.17521/cjpe.2015.0030

摘要/Abstract

摘要：

快速、定量、精确地估算区域森林生物量一直是森林生态功能评价以及碳储量研究的重要问题。该研究基于机载激光雷达(LiDAR)点云与Landsat 8 OLI多光谱数据, 借助江苏省常熟市虞山地区55块调查样地数据, 首先提取并分析了87个特征变量(53个OLI特征变量, 34个LiDAR特征变量)与森林地上、地下生物量的Pearson’s相关系数以进行变量优选, 然后利用多元逐步回归法建立森林生物量估算模型(OLI生物量估算模型和LiDAR生物量估算模型), 并与基于两种数据建立的综合生物量估算模型的结果进行比较, 讨论预测结果及其精确性。结果表明: 3种模型(OLI模型、LiDAR模型和综合模型)在所有样地无区分分析时, 地上和地下生物量的估算精度均达到0.4以上, 基于不同森林类型(针叶林、阔叶林、混交林)分析时地上和地下生物量的估算精度均有明显提高, 达到0.67及以上。利用分森林类型模型估算生物量, 综合生物量估算模型精度(地上生物量: R²为0.88; 地下生物量: R²为0.92)优于OLI生物量估算模型(地上生物量: R²为0.73; 地下生物量: R²为0.81)和LiDAR生物量估算模型(地上生物量: R²为0.86; 地下生物量: R²为0.83)。

关键词: 森林生物量估算, 亚热带森林, OLI多光谱数据, 机载小光斑激光雷达数据, 逐步回归

Abstract: Aims

Estimating forest biomass at regional scale with high accuracy is among the pressing challenges in evaluating ecosystem functions and characteristics (e.g., carbon storage).

Methods

This study is based on airborne small-footprint discrete-return LiDAR data, Landsat 8 OLI multispectral data, and in situ measurements from 55 forest plots in Yushan, Changshu, Jiangsu Province. A total of 87 independent variables (53 from OLI metrics and 34 from LiDAR metrics) were used in the Pearson correlation analysis for estimating aboveground (W_A) and belowground (W_B) biomass by identifying the significant independent variables. Three independent models by using OLI, LiDAR and their combinations (i.e., the combo model) were established through stepwise regression analysis.

Important findings

The correlation coefficients of determination (R²) for W_A and W_B models are greater than 0.4. The R² seemed much higher when the estimations were type-specific (e.g., coniferous, broad-leaf and mixed forest), with R² of >0.67. The Combo model by forest type yielded an R² of 0.88 for W_A and 0.92 for W_B, while the OLI-based model had R² of 0.73 and 0.81 for W_A and W_B, respectively. The LiDAR-based model has R² of 0.86 and 0.83 for W_A and W_B, respectively.

Key words: estimate of forest biomass, subtropical forest, OLI multispectral data, air borne small-footprint LiDAR data, stepwise regression

徐婷, 曹林, 申鑫, 佘光辉. 基于机载激光雷达与Landsat 8 OLI数据的亚热带森林生物量估算. 植物生态学报, 2015, 39(4): 309-321. DOI: 10.17521/cjpe.2015.0030

XU Ting,CAO Lin,SHEN Xin,SHE Guang-Hui. Estimates of subtropical forest biomass based on airborne LiDAR and Landsat 8 OLI data. Chinese Journal of Plant Ecology, 2015, 39(4): 309-321. DOI: 10.17521/cjpe.2015.0030

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2015.0030

https://www.plant-ecology.com/CN/Y2015/V39/I4/309

图/表 11

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森林参数 Forest metrics	针叶林 Coniferous forest (n = 13)			阔叶林 Broad-leaf forest (n = 16)			混交林 Mixed forest (n = 26)
森林参数 Forest metrics	数值范围 Range	平均值 Mean	标准偏差 SD	数值范围 Range	平均值 Mean	标准偏差 SD	数值范围 Range	平均值 Mean	标准偏差 SD
地上生物量 W_A (t·hm^-2)	47.66-102.76	76.52	18.44	32.03-151.45	91.02	31.74	49.65-192.22	87.28	28.22
地下生物量 W_B (t·hm^-2)	14.42-32.59	22.81	5.34	10.31-37.39	26.27	6.08	15.69-61.89	25.82	8.41

森林参数 Forest metrics	针叶林 Coniferous forest (n = 13)			阔叶林 Broad-leaf forest (n = 16)			混交林 Mixed forest (n = 26)
森林参数 Forest metrics	数值范围 Range	平均值 Mean	标准偏差 SD	数值范围 Range	平均值 Mean	标准偏差 SD	数值范围 Range	平均值 Mean	标准偏差 SD
地上生物量 W_A (t·hm^-2)	47.66-102.76	76.52	18.44	32.03-151.45	91.02	31.74	49.65-192.22	87.28	28.22
地下生物量 W_B (t·hm^-2)	14.42-32.59	22.81	5.34	10.31-37.39	26.27	6.08	15.69-61.89	25.82	8.41

特征变量 Metrics	变量描述 Description
原始单波段 Initial bands
B₂-B₇	OLI第2-7波段(经过大气校正和几何精校正) Second to seventh band from OLI (after atmospheric correction and geometric exact correction)
波段组合 Band combination
Albedo	Albedo = B₂ + B₃ + B₄ + B₅ + B₆ + B₇
B₄/Albedo	B₄/Albedo = B₄ / (B₂ + B₃ + B₄ + B₅ + B₆ + B₇)
B₂₄	B₂₄= B₂ / B₄
B₇₄	B₇₄= B₇ / B₄
B₇₆	B₇₆= B₇ / B₆
B₅₄₇	B₅₄₇= B₅ · B₄/ B₇
B₆₅	B₆₅= B₆ / B₅
B₃₄₅	B₃₄₅= B₃ · B₄/ B₅
B₅₃	B₅₃= B₅ / B₃
VIS₂₃₄	VIS₂₃₄ = B₂ + B₃ + B₄
信息增强组 Information enhance
绿度 Greenness (TC_G)	提取缨帽变换绿度波段 Extract greenness from tasseled cap transform
亮度 Brightness (TC_B)	提取缨帽变换亮度波段 Extract brightness from tasseled cap transform
湿度 Wetness (TC_W)	提取缨帽变换湿度波段 Extract wetness from tasseled cap transform
第一主成分 First principal component (PC₁)	提取主成分分析第一波段 Extract first band from principal component analysis
第二主成分 Second principal component (PC₂)	提取主成分分析第二波段 Extract second band from principal component analysis
第三主成分 Third principal component (PC₃)	提取主成分分析第三波段 Extract third band from principal component analysis
最小噪声分离变换第一波段 First band of minimum noise fraction rotation (MNF₁)	提取MNF变换第一波段 Extract first band from minimum noise fraction rotation
最小噪声分离变换第二波段 Second band of minimum noise fraction rotation (MNF₂)	提取MNF变换第二波段 Extract second band from minimum noise fraction rotation
最小噪声分离变换第三波段 Third band of minimum noise fraction rotation (MNF₃)	提取MNF变换第三波段 Extract third band from minimum noise fraction rotation
最小噪声分离变换第四波段 Forth band of minimum noise fraction rotation (MNF₄)	提取MNF变换第四波段 Extract forth band from minimum noise fraction rotation
植被指数 Vegetation index
增强型植被指数 Enhanced vegetation index (EVI)
归一化植被指数 Normalized difference vegetation index (NDVI)
土壤调整植被指数 Soil-adjusted vegetation index (SAVI)
有效叶面积指数 Specific leaf area vegetation index (SLAVI)
简单比值植被指数 Ratio vegetation index (RVI)
中红外植被指数 Mid-infrared vegetation index (VI3)
垂直植被指数 Perpendicular vegetation index (PVI)
土壤调整比值植被指 Soil-adjusted ratio vegetation index (SARVI)
差值植被指数 Difference vegetation index (DVI)
转换型植被指数 Transformed normalized difference vegetation index (TNDVI)
大气阻抗植被指数 Atmospherically resistant vegetation index (ARVI)
修正型土壤调整植被指数 Modified soil-adjusted vegetation index (MSAVI)
修正型简单比值植被指数 Modified simple ratio vegetation index (MSR)
非线性指数 Nonlinear index (NLI)
重归一化植被指数 Renormalized difference vegetation index (RDVI)
归一化植被指数 Normalized difference vegetation index (ND₄₃)
归一化植被指数 Normalized difference vegetation index (ND₆₇)
归一化植被指数 Normalized difference vegetation index (ND₅₆₃)
纹理信息 Texture information
相关度 Correlation (CR)
对比度 Contrast (CO)
相异性 Dissimilarity (DI)
信息熵 Entropy (EN)
均匀度 Homogeneity (HO)
均值 Mean (ME)
二阶矩 Second moment (SM)
偏斜度 Skewness (SK)
方差 Variance (VA)

特征变量 Metrics	变量描述 Description
原始单波段 Initial bands
B₂-B₇	OLI第2-7波段(经过大气校正和几何精校正) Second to seventh band from OLI (after atmospheric correction and geometric exact correction)
波段组合 Band combination
Albedo	Albedo = B₂ + B₃ + B₄ + B₅ + B₆ + B₇
B₄/Albedo	B₄/Albedo = B₄ / (B₂ + B₃ + B₄ + B₅ + B₆ + B₇)
B₂₄	B₂₄= B₂ / B₄
B₇₄	B₇₄= B₇ / B₄
B₇₆	B₇₆= B₇ / B₆
B₅₄₇	B₅₄₇= B₅ · B₄/ B₇
B₆₅	B₆₅= B₆ / B₅
B₃₄₅	B₃₄₅= B₃ · B₄/ B₅
B₅₃	B₅₃= B₅ / B₃
VIS₂₃₄	VIS₂₃₄ = B₂ + B₃ + B₄
信息增强组 Information enhance
绿度 Greenness (TC_G)	提取缨帽变换绿度波段 Extract greenness from tasseled cap transform
亮度 Brightness (TC_B)	提取缨帽变换亮度波段 Extract brightness from tasseled cap transform
湿度 Wetness (TC_W)	提取缨帽变换湿度波段 Extract wetness from tasseled cap transform
第一主成分 First principal component (PC₁)	提取主成分分析第一波段 Extract first band from principal component analysis
第二主成分 Second principal component (PC₂)	提取主成分分析第二波段 Extract second band from principal component analysis
第三主成分 Third principal component (PC₃)	提取主成分分析第三波段 Extract third band from principal component analysis
最小噪声分离变换第一波段 First band of minimum noise fraction rotation (MNF₁)	提取MNF变换第一波段 Extract first band from minimum noise fraction rotation
最小噪声分离变换第二波段 Second band of minimum noise fraction rotation (MNF₂)	提取MNF变换第二波段 Extract second band from minimum noise fraction rotation
最小噪声分离变换第三波段 Third band of minimum noise fraction rotation (MNF₃)	提取MNF变换第三波段 Extract third band from minimum noise fraction rotation
最小噪声分离变换第四波段 Forth band of minimum noise fraction rotation (MNF₄)	提取MNF变换第四波段 Extract forth band from minimum noise fraction rotation
植被指数 Vegetation index
增强型植被指数 Enhanced vegetation index (EVI)
归一化植被指数 Normalized difference vegetation index (NDVI)
土壤调整植被指数 Soil-adjusted vegetation index (SAVI)
有效叶面积指数 Specific leaf area vegetation index (SLAVI)
简单比值植被指数 Ratio vegetation index (RVI)
中红外植被指数 Mid-infrared vegetation index (VI3)
垂直植被指数 Perpendicular vegetation index (PVI)
土壤调整比值植被指 Soil-adjusted ratio vegetation index (SARVI)
差值植被指数 Difference vegetation index (DVI)
转换型植被指数 Transformed normalized difference vegetation index (TNDVI)
大气阻抗植被指数 Atmospherically resistant vegetation index (ARVI)
修正型土壤调整植被指数 Modified soil-adjusted vegetation index (MSAVI)
修正型简单比值植被指数 Modified simple ratio vegetation index (MSR)
非线性指数 Nonlinear index (NLI)
重归一化植被指数 Renormalized difference vegetation index (RDVI)
归一化植被指数 Normalized difference vegetation index (ND₄₃)
归一化植被指数 Normalized difference vegetation index (ND₆₇)
归一化植被指数 Normalized difference vegetation index (ND₅₆₃)
纹理信息 Texture information
相关度 Correlation (CR)
对比度 Contrast (CO)
相异性 Dissimilarity (DI)
信息熵 Entropy (EN)
均匀度 Homogeneity (HO)
均值 Mean (ME)
二阶矩 Second moment (SM)
偏斜度 Skewness (SK)
方差 Variance (VA)

特征变量 Metrics	变量描述 Description
高度百分位数 Percentile height
h₁₀, h₂₀, h_25,h₃₀, h₄₀, h₅₀, h₆₀, h₇₀, h₇₅, h₈₀, h_90, h_95, h₉₉	第一回波返回点的冠层高度分布百分位数(10th, 20th, 25th, 30th, 40th, 50th, 60th, 70th, 75th, 80th, 90th, 95th, 99th) The percentiles of the canopy height distributions (10th, 20th,…99th) of first returns
高度变量 Height metrics
h_min	激光雷达树高的最小值 Minimum height above ground of all first returns
h_skew	激光雷达树高的偏斜度 Skewness of heights of all first returns
h_kur	激光雷达树高的峰度 Kurtosis of heights of all first returns
h_iq	激光雷达树高的75%和25%分位数的差值 Difference between 75% and 25% of canopy height of first returns
h_mean	激光雷达树高的平均值 Mean height above ground of all first returns
h_max	激光雷达树高的最大值 Maximum height above ground of all first returns
h_cv	激光雷达树高的高度变异系数 Coefficient of variation of heights of all first returns
h_mode	激光雷达树高的众数 Mode of heights of all first returns
h_var	激光雷达树高的方差 Variance of heights of all first returns
h_std	激光雷达树高的标准差 Standard deviation of heights of all first returns
冠层密度 Canopy density
d₀, d₁, d₂, d₃, d₄, d₅, d₆, d₇, d₈, d₉	高于一定范围相对高度的冠层返回密度, 即第一回波中高于(0、10、20、…90)分位数的点占第一回波所有点的百分比(0-100%) The canopy return density over a range of relative heights, i.e., percentage (0%-100%) of first returns above the quantiles (0, 10, 20…90) to total number of first returns
覆盖度 Cover
c	取高于2 m的冠层返回点在所有激光返回点中所占的比例 Percentages of first returns above 2 m