Evaluation of forest ecological quality in coastal zone counties of Fujian Province, eastern China based on multi-source remote sensing data
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摘要:目的
森林生态质量是从生态角度反映森林质量的内涵，对森林的生态功能和生态服务、生长状况以及自我调节功能进行综合测度，以期提高森林改善生态环境、维护生态平衡的能力。
方法利用中、高分辨率多源遥感数据，获取大范围尺度下能表征森林生态质量的关键指标信息，在此基础上，分析福建省海岸带40个县域的森林生态质量状况。首先，基于2016年2 m分辨率多源遥感数据为主要数据源，利用双层尺度集模型选定最佳分割尺度，多分类器集成算法集，自动选择最优分类算法进行森林类型提取，并结合2020年Sentinel遥感数据及森林分类产品，更新2020年福建省海岸带森林类型精细分布图；其次，利用LandTrendr算法衍生的干扰开始时间特征推算现存森林年龄，通过GEDI冠层高度产品获取海岸带森林冠层高度分布图；在以上关键森林质量指标提取基础上，对遥感手段获取的8项森林生态质量评价指标进行主成分分析，获得福建省海岸带县域森林生态质量综合评价结果。
结果2020年福建海岸带40个县域约50%的县域森林生态质量处于优良水平，其中仙游县、闽侯县、南安市、霞浦县、柘荣县及厦门海沧区、思明区、集美区、同安区等森林生态质量为优；森林生态质量较差的县域有惠安县、秀屿区、石狮市、福安市、平潭实验区、诏安县。
结论结合中、高分辨率多源遥感数据，能够发挥遥感大范围监测优点，客观评价福建省海岸带40个县域的森林生态质量；研究结果表明2020年福建沿海县域森林生态质量还存在较大的提升空间，需要针对存在的问题采取相应森林管理措施提升森林生态质量。
Abstract:ObjectiveForest ecological quality reflects the ecological quality of forests from an ecological perspective, measuring the comprehensive capacity of forests to improve the ecological environment, maintain ecological balance, and provide ecological functions and services. Utilizing medium and high-resolution multisource remote sensing data, we aim to acquire crucial indicator information that characterizes forest ecological quality at a large-scale level. Based on this foundation, our objective is to analyze the forest ecological quality status in the 40 coastal counties of Fujian Province of eastern China.
MethodFirstly, the primary data source utilized was 2 m resolution multisource remote sensing data. The bi-level scale-sets model (BSM) was employed to determine the optimal segmentation scale, integrating multiple classifiers to create an algorithm set. The best classification algorithm for forest type extraction was automatically selected. This approach was complemented by the use of Sentinel remote sensing data in 2020 and fine forest classification products, resulting in an optimized fine-scale distribution map of coastal forests in Fujian Province for year 2020. Secondly, forest age was estimated using disturbance onset time features derived from the LandTrendr algorithm. Additionally, GEDI crown height products were used to obtain the canopy height distribution map of coastal forests. Building upon the extraction of key forest quality indicators through remote sensing methods, a principal component analysis was conducted on eight forest ecological quality assessment indicators obtained through remote sensing. This led to a comprehensive assessment of forest ecological quality for the coastal counties of Fujian Province.
ResultIn 2020, approximately 50% of the 40 coastal counties in Fujian’s coastal zone exhibited a favorable level of forest ecological quality. Among them, Xianyou County, Minhou County, Nan’an City, Xiapu County, Zherong County, as well as the districts of Haicang, Siming, Jimei, and Tong’an in Xiamen, demonstrated excellent forest ecological quality. On the other hand, counties with relatively poor forest ecological quality included Huian County, Xiuyu District, Shishi City, Fu’an City, Pingtan Experimental Zone, and Zhao’an County.
ConclusionUtilizing high and medium-resolution multisource remote sensing data, the advantages of remote sensing large-scale monitoring have been leveraged to provide an objective assessment of the forest ecological quality in the 40 coastal counties of Fujian Province. The research findings reveal that there is considerable room for improvement in the forest ecological quality of the coastal counties in Fujian Province in 2020. Addressing the existing issues calls for the implementation of appropriate forest management measures to enhance the forest ecological quality.
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Keywords:
- remote sensing /
- forest type /
- forest age /
- canopy height /
- forest ecology quality /
- Fujian coastal zone
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图 2 基于多源遥感数据的2020年福建海岸带森林类型分布提取结果
Figure 2. Extraction results of forest type distribution in Fujian Province coastal zone in 2020 based on multi-source remote sensing data
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图 3 2020年福建海岸带森林类型面积统计及森林覆盖率
Figure 3. Forest area statistics and forest coverage of coastal zone in Fujian Province in 2020
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图 5 福建海岸带森林林龄空间分布
Figure 5. Spatial distribution of forest age in Fujian Province coastal zone
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图 7 福建海岸带GEDI冠层高度分级
Figure 7. GEDI canopy height classification of Fujian Province coastal zone
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图 8 福建海岸带各县域森林生态质量综合指数
Figure 8. Comprehensive index of forest ecological quality by counties in Fujian Province coastal zone
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图 9 福建海岸带县域尺度森林生态质量等级分布
Figure 9. Distribution of forest ecological quality levels at the county level in the coastal zone of Fujian Province
表 1 遥感影像数据介绍
Table 1 Introduction of remote sensing image data
卫星名称
Satellite name空间分辨率
Spatial resolution景数
Scene number获取时间
Access time资源三号 ZY-3 2.1 m （全色）
2.1 m （panchromatic）
5.8 m （多光谱）
5.8 m （multispectral）30 2015年9月至2016年9月，以2016年度卫星影像为主
September 2015 to September 2016, based on
annual satellite imagery in 2016高分一号 GF-1 2 m （全色）
2 m （panchromatic）
8 m （多光谱）
8 m （multispectral）19 天绘一号 TH-1 2 m （全色）
2 m （panchromatic）
10 m （多光谱）
8 m （multispectral）13 Landsat5、Landsat7、Landsat8 30 m 每年约20 ~ 40景
About 20−40 views per year1991—2020年7—10月
July to October in 1991 to 2020
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表 2 LandTrendr算法参数
Table 2 Parameters of LandTrendr algorithm
过程
Step参数
Parameter值
Value分割
Segmentation核尺寸 Kernel size 3 最大分段数
Maximum number of segmentation6 噪声值 Noise level 0.9 恢复率阈值 Recovery rate threshold 0.25 最优模型比例 Optimal model ratio 0.75 滤波
Filter1年植被覆盖损失阈值
1 year vegetation cover loss threshold10 10年植被覆盖损失阈值
10 year vegetation cover loss threshold3 干扰前覆盖阈值
Pre-interference coverage threshold20 植被生长比例阈值
Threshold for percentage of vegetation growth5 制图
Mapping最小制图单位 Minimum mapping unit 30 m
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表 3 森林生态质量评价指标体系
Table 3 Forest ecological quality evaluation index system
指标
Index森林生态质量评价指标含义
Meaning of forest ecological quality assessment indicators计算方法
Calculation method森林类型
Forest type (X1)表征森林覆盖类型的均衡程度
Characterizing the balance of forest cover types森林各类型面积与其区域面积比
Proportion of area of each forest type in relation to
its regional area森林覆盖率
Forest coverage (X2)反映地区森林资源、林业发展状况及森林效益的综合性指标
Comprehensive indicators reflecting regional forest resources,
forestry development and forest benefits区域森林面积与其区域面积比值
Ratio of regional forest area to its regional area龄级结构
Age structure (X3)反映森林龄级的均衡水平
Reflecting equilibrium level of forest age class各龄级与森林面积比值
Ratio of age classes to forest area人工林比重
Percentage of plantation (X4)反映森林资源造林活动的演替程度
Reflecting the degree of succession of silvicultural
activities on forest resources人工林面积与森林面积比值
Ratio of plantation area to forest area人均森林面积
Forest area per capita (X5)反映森林资源的人均占有量水平
Reflecting the level of per capita occupancy of forest resources森林总面积与人口比值
Ratio of total forest area to population林地利用率
Forest land utilization rate (X6)反映林业用地的利用水平
Reflecting the level of utilization of forestry land有林地面积与林业用地面积比值
Ratio of forested land area to forest land area冠层高度
Canopy height (X7)反映森林生长状况
Reflecting forest growth condition各冠层高度面积与森林总面积比值
Ratio of area of each canopy height to
total forest area干扰量级
Interference level (X8)反映森林受干扰的程度
Reflecting the extent of forest disturbance不同干扰量级与干扰总面积比值
Ratio of different interference levels to
total interference area
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表 4 福建海岸带森林类型遥感分类结果精度验证
Table 4 Validation of the accuracy of forest classification results in the coastal zone of Fujian Province
地区
Region精度
Accuracy人工地表
Artificial
surface耕地
Plow
land迹地
Trails有林地
Woodland果园
Orchard茶园
Tea
plantation水体
Waters红树林
Mangrove总体精度
Overall
accuracy/%Kappa系数
Kappa
coefficient宁德市
Ningde CityPA/% 94.12 91.89 88.89 92.86 88.46 88.24 95.83 91.11 0.89 UA/% 91.43 82.93 92.31 94.20 85.19 91.84 95.83 F 92.75 87.18 90.57 93.53 86.79 90.00 95.83 福州市
Fuzhou CityPA/% 92.31 88.10 89.74 96.25 88.24 87.10 97.37 92.71 0.91 UA/% 85.71 86.05 94.59 95.06 93.75 93.10 97.37 F 88.89 87.06 92.11 95.65 90.91 90.00 97.37 莆田市
Putian CityPA/% 92.31 94.00 91.67 92.31 85.71 87.18 94.87 91.51 0.89 UA/% 96.00 94.00 91.67 88.89 85.71 89.47 97.37 F 94.12 94.00 91.67 90.57 85.71 88.31 96.10 泉州市
Quanzhou CityPA/% 89.13 93.88 85.71 91.30 86.11 84.62 96.30 93.33 90.20 0.88 UA/% 95.35 85.19 92.31 95.45 86.11 81.48 92.86 87.50 F 92.13 89.32 88.89 93.33 86.11 83.02 94.55 90.32 厦门市
Xiamen CityPA/% 89.19 91.67 90.91 93.88 83.33 92.31 96.00 91.96 0.90 UA/% 91.67 91.67 90.91 93.88 88.24 92.31 92.31 F 90.41 91.67 90.91 93.88 85.71 92.31 94.12 漳州市
Zhangzhou CityPA/% 93.18 93.33 91.67 93.65 87.10 87.23 91.67 93.75 91.32 0.90 UA/% 93.18 91.80 91.67 92.19 88.52 89.13 93.62 90.91 F 93.18 92.56 91.67 92.91 87.80 88.17 92.63 92.31 注：PA、UA、F分别代表生产者精度、用户精度和F统计量。Notes: PA, UA, and F represent producer accuracy, user accuracy, and F statistic, respectively.
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表 5 森林生态质量评价指标Pearson相关系数
Table 5 Pearson correlation coefficients of forest ecological quality evaluation indicators
指标
Index平均值
Average value标准差
Standard deviationX1 X2 X3 X4 X5 X6 X7 X8 X1 0.794 0.269 1 X2 0.556 0.329 −0.657** 1 X3 0.569 0.216 −0.039 −0.13 1 X4 0.325 0.178 −0.058 0.156 −0.241 1 X5 0.225 0.233 −0.643** 0.821** −0.119 −0.028 1 X6 0.795 0.276 0.524** −0.485** −0.044 0.001 −0.475** 1 X7 0.578 0.271 0.414** −0.830** 0.005 −0.041 −0.736** 0.207 1 X8 0.209 0.236 −0.558** 0.689** −0.064 0.194 0.559** −0.29 −0.571** 1 注：* P < 0.05 ，** P < 0.01。Notes: * P < 0.05 ，** P < 0.01.
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表 6 线性组合系数及权重结果
Table 6 Results of linear combination coefficients and weights
名称Name Y1 Y2 Y3 Y4 综合得分系数
Composite score coefficient权重
Weight/%X1 0.2929 0.5220 0.083 0.0892 0.2846 13.92 X2 0.4935 0.2824 0.0875 0.1021 0.3218 15.74 X3 0.026 0 0.0250 0.1289 0.9689 0.1823 8.92 X4 0.0277 0.0033 0.9321 0.1354 0.1783 8.72 X5 0.4534 0.3146 0.1397 0.1475 0.3260 15.95 X6 0.0827 0.7208 0.0280 0.0002 0.2141 10.48 X7 0.5295 0.0081 0.0485 0.0169 0.2540 12.43 X8 0.4201 0.1688 0.2783 0.0506 0.2831 13.85 特征根 Eigenvalue 3.149 1.643 1.069 1.027 方差解释率
Variance explaination rate/%39.37 20.54 13.37 12.84
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