基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究

doi:10.17521/cjpe.2020.0226

植物生态学报 ›› 2021, Vol. 45 ›› Issue (4): 355-369.DOI: 10.17521/cjpe.2020.0226

基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究

周明星¹, 李登秋²^,^*(), 邹建军¹

¹浙江农林大学环境与资源学院, 浙江省森林生态系统碳循环与固碳减排重点实验室, 浙江临安 311300
²福建师范大学湿润亚热带山地生态国家重点实验室培育基地, 福建师范大学地理科学学院, 福州 350007

收稿日期:2020-07-08 接受日期:2021-02-04 出版日期:2021-04-20 发布日期:2021-03-27
通讯作者: ORCID: *李登秋: 0000-0002-3971-3222(lidengqiu001@163.com)
作者简介:* lidengqiu001@163.com
基金资助:
国家重点研发计划(2016YFC0503302);国家自然科学基金(41701490)

Vegetation change of giant panda habitats in Qionglai Mountains through dense Landsat Data

ZHOU Ming-Xing¹, LI Deng-Qiu²^,^*(), ZOU Jian-Jun¹

¹School of Environmental and Resource Sciences, Zhejiang A&F University, Key Laboratory of Carbon Cycling in Forest Ecosystem and Carbon Sequestration of Zhejiang Province, Lin’an, Zhejiang 311300, China
²State Key Laboratory for Subtropical Mountain Ecology of the Ministry of Science and Technology and Fujian Province, School of Geographical Sciences, Fujian Normal University, Fuzhou 350007, China

Received:2020-07-08 Accepted:2021-02-04 Online:2021-04-20 Published:2021-03-27
Contact: LI Deng-Qiu
Supported by:
National Key R&D Program of China(2016YFC0503302);National Natural Science Foundation of China(41701490)

1. 附录I 邛崃山大熊猫栖息地植被变化驱动因子具体分区范围.pdf(377KB)

摘要/Abstract

摘要：

深入理解大熊猫栖息地植被变化过程及其驱动力, 是开展大熊猫栖息地保护和管理的重要基础。该研究利用1986- 2018年所有可用的Landsat TM/ETM/OLI影像构建长时间序列归一化植被指数(NDVI), 采用BFAST (Breaks For Additive Seasonal and Trend)方法实现大熊猫栖息地植被变化历史检测, 从植被累积突变、累积渐变和总变化3个指标揭示植被变化空间分布特征; 运用地理探测模型定量评价不同因子(年降水量、年平均气温、高程、坡度、坡向、与河流距离、土壤类型、土地覆盖类型、与道路的距离、与工程扰动区距离)对3种植被变化空间分布的影响。结果表明: 1)研究区内植被突变面积比例为9.13%, 主要分布于栖息地东部边界附近, 2011和2013年植被突变面积较大; 2)植被累积突变表现为退化面积占植被累积突变面积的40.17%, 植被累积渐变和总变化表明研究区植被呈现改善趋势, 改善面积比例分别占研究区的94.58%和97.02%; 3) 3种植被变化的空间分布主要受年降水量、年平均气温、高程、土壤类型4种因子的影响, 植被累积突变、累积渐变和总变化空间分布的最强解释因子分别为年降水量、高程和土壤类型, 驱动因子之间的交互作用为相互增强、非线性增强关系。

关键词: Breaks For Additive Seasonal and Trend (BFAST), 地理探测模型, 植被变化, 归一化植被指数, 大熊猫栖息地

Abstract:

Aims Understanding the processes and drivers of vegetation change in giant panda habitats plays an important role in their conservation and management.

Methods Based on the long-term normalized difference vegetation index (NDVI) time series that was constructed by all available Landsat TM/ETM/OLI images from 1986 to 2018, we employed the BFAST (Breaks For Additive Seasonal and Trend) method and harmonic model to monitor the vegetation change during the period of 1986-2018. Three types of NDVI changes (i.e. vegetation accumulated abrupt change, accumulated gradual change, and total change) were built to reveal the spatial distribution characteristics of vegetation change. The effects of different factors (i.e. mean annual precipitation, mean annual air temperature, elevation, slope, aspect, distance to rivers, soil type, land cover type, distance to roads and distance to engineering disturbance area) on the spatial distribution of the three types of vegetation change were evaluated by Geodetector.

Important findings 1) A total of 9.13% of vegetation abrupt change in the study area was detected, which was mainly distributed around the eastern boundary of the habitats, and the largest abrupt change areas occurred in 2011 and 2013. 2) The proportion of vegetation accumulated abrupt change showing degradation accounted for 40.17% of the vegetation accumulated abrupt change area, and the accumulated gradual change and total change which presented increasing trends accounted for 94.58% and 97.02% of the study area, respectively. 3) The spatial distribution of vegetation changes was mainly affected by four factors: mean annual precipitation, mean annual air temperature, elevation, and soil type. The strongest explanatory factors of vegetation accumulated abrupt change, accumulated gradual change, and total change were mean annual precipitation, elevation, and soil type, respectively. The interactions between driving factors were mutually enhanced and nonlinearly enhanced.

Key words: Breaks For Additive Seasonal and Trend (BFAST), Geodetector, vegetation change, normalized difference vegetation index (NDVI), giant panda habitat

周明星, 李登秋, 邹建军. 基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究. 植物生态学报, 2021, 45(4): 355-369. DOI: 10.17521/cjpe.2020.0226

ZHOU Ming-Xing, LI Deng-Qiu, ZOU Jian-Jun. Vegetation change of giant panda habitats in Qionglai Mountains through dense Landsat Data. Chinese Journal of Plant Ecology, 2021, 45(4): 355-369. DOI: 10.17521/cjpe.2020.0226

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2020.0226

https://www.plant-ecology.com/CN/Y2021/V45/I4/355

图/表 11

图1 邛崃山大熊猫栖息地研究区位置。大熊猫栖息地(包含潜在栖息地)范围来源于全国第四次大熊猫调查。

Fig. 1 Location of the study area of giant panda habitats in Qionglai Mountains. The boundary of giant panda habitats (including potential habitats) is from the Fourth National Giant Panda Survey.

表1 邛崃山大熊猫栖息地植被变化的驱动因子

Table 1 Drivers of vegetation change of giant panda habitats in Qionglai Mountains

因子类型 Factor type	因子 Factor	空间分辨率 Spatial resolution (m)	预处理(重采样) Preprocessing (resampling)
气候 Climate	年降水量 Mean annual precipitation (X1)	1 000	双线性内插法 Bilinear interpolation
气候 Climate	年平均气温 Mean annual air temperature (X2)	1 000	双线性内插法 Bilinear interpolation
地形 Topography	高程 Elevation (X3)	30	-
	坡度 Slope (X4)	30	-
	坡向 Aspect (X5)	30	-
河流 River	与河流距离 Distance to rivers (X6)	30	-
土壤类型 Soil type	土壤类型 Soil type (X7)	1 000	最邻近内插法 Nearest interpolation
人类活动 Human activity	土地覆盖类型 Land cover type (X8)	30	-
	与道路距离 Distance to roads (X9)	30	-
	与工程扰动区距离 Distance to engineering disturbance area (X10)	30	-

表2 1986-2018年邛崃山大熊猫栖息地植被不同突变次数的面积比例

Table 2 Area proportion for various number of vegetation abrupt change of giant panda habitats in Qionglai Mountains during 1986-2018

突变次数 Number of abrupt change	占研究区比例 Proportion of study area (%)		占突变区域比例 Proportion of abrupt change area (%)
突变次数 Number of abrupt change	栖息地 Habitat	外围区域 Peripheral area	栖息地 Habitat	外围区域 Peripheral area
0	58.09	32.78	-	-
1	5.74	2.92	62.87	31.98
2	0.24	0.21	2.63	2.30
3	0.01	0.01	0.11	0.11
4	0.00	0.00	0.00	0.00
≥1	5.99	3.14	65.61	34.39

图2 1986-2018年邛崃山大熊猫栖息地植被突变空间分布。A, 发生次数。B, 最近一次检测到突变的发生年份。

Fig. 2 Spatial distribution of vegetation abrupt change of giant panda habitats in Qionglai Mountains during 1986-2018. A, Number of abrupt change. B, Year of the latest abrupt change.

图3 1986-2018年邛崃山大熊猫栖息地植被变化。A, 植被累积突变。B, 植被累积渐变。C, 植被总变化。

Fig. 3 Vegetation change of giant panda habitats in Qionglai Mountains during 1986-2018. A, Vegetation accumulated abrupt change. B, Vegetation accumulated gradual change. C, Vegetation total change.

表3 邛崃山大熊猫栖息地因子探测器结果(q)

Table 3 Results of factor detector of giant panda habitats in Qionglai Mountains (q)

因子 Factor	植被累积突变 Vegetation accumulated abrupt change			植被累积渐变 Vegetation accumulated gradual change			植被总变化 Vegetation total change
因子 Factor	q	p(sig)	q排序 Sequence of q	q	p(sig)	q排序 Sequence of q	q	p(sig)	q排序 Sequence of q
X1	0.048 1	2.78E-10	1	0.031 5	6.49E-11	4	0.068 1	6.44E-10	5
X2	0.021 3	2.89E-10	2	0.037 6	6.14E-12	2	0.089 1	2.69E-11	3
X3	0.018 9	1.56E-10	3	0.037 7	5.38E-10	1	0.089 6	8.18E-10	2
X4	0.001 1	5.61E-11	10	0.000 3	2.78E-10	10	0.001 5	5.49E-10	10
X5	0.001 4	5.03E-10	9	0.001 6	2.10E-10	9	0.005 8	1.87E-10	9
X6	0.011 7	4.07E-11	5	0.012 3	6.59E-10	6	0.028 2	3.72E-10	6
X7	0.013 6	3.11E-11	4	0.037 5	1.95E-10	3	0.097 4	6.81E-10	1
X8	0.005 9	5.49E-10	7	0.023 8	4.22E-10	5	0.079 7	3.07E-10	4
X9	0.010 6	2.98E-10	6	0.008 0	6.97E-11	7	0.018 3	9.47E-10	7
X10	0.002 5	2.20E-11	8	0.003 1	6.36E-10	8	0.010 3	2.07E-10	8

表4 邛崃山大熊猫栖息地交互作用探测结果(每种植被变化选择前9个较大的q并进行排序)

Table 4 Results of interaction detector (the first nine larger q were selected and ranked for each vegetation change) of giant panda habitats in Qionglai Mountains

植被累积突变 Vegetation accumulated abrupt change		植被累积渐变 Vegetation accumulated gradual change		植被总变化 Vegetation total change
因子 Factor	q	因子 Factor	q	因子 Factor	q
X1∩X6	0.071 4^*	X7∩X1	0.052 5	X7∩X3	0.134 8
X1∩X7	0.068 9^*	X7∩X3	0.050 2	X7∩X8	0.133 4
X1∩X2	0.065 3	X7∩X2	0.049 7	X7∩X2	0.131 9
X1∩X3	0.064 0	X7∩X8	0.047 5	X7∩X1	0.130 2
X1∩X9	0.061 9^*	X3∩X1	0.045 7	X8∩X3	0.122 6
X1∩X10	0.061 5^*	X8∩X3	0.045 0	X8∩X2	0.121 5
X1∩X8	0.057 0^*	X8∩X2	0.045 0	X7∩X6	0.117 8
X1∩X5	0.051 9^*	X8∩X1	0.044 9	X7∩X9	0.114 5
X1∩X4	0.051 0^*	X7∩X6	0.044 4	X8∩X1	0.113 8

图4 邛崃山大熊猫栖息地植被累积突变的风险探测器结果。不同小写字母表示不同分区之间存在显著差异(p< 0.05), 因子(X1-X10)同表1。

Fig. 4 Risk detection result of vegetation accumulated abrupt change of giant panda habitats in Qionglai Mountains. Different lowercase letters indicate significant differences between the different partitions (p < 0.05). The factors (X1-X10) are the same as Table 1.

图5 邛崃山大熊猫栖息地植被累积渐变的风险探测结果。不同小写字母表示不同分区之间存在显著差异(p < 0.05), 因子(X1-X10)同表1。

Fig. 5 Risk detection result of vegetation accumulated gradual change of giant panda habitats in Qionglai Mountains. Different lowercase letters indicate significant differences between the different partitions (p < 0.05). The factors (X1-X10) are the same as Table 1.

图6 邛崃山大熊猫栖息地植被总变化的风险探测结果。不同小写字母表示不同分区之间存在显著差异(p < 0.05), 因子(X1-X10)同表1。

Fig. 6 Risk detection result of vegetation total change of giant panda habitats in Qionglai Mountains. Different lowercase letters indicate significant differences between the different partitions (p < 0.05). The factors (X1-X10) are the same as Table 1.

图7 邛崃山大熊猫栖息地植被突变对植被变化因子探测器结果的影响(q)。区域I代表检测到植被突变的区域, 区域II代表未检测到植被突变的区域, 因子(X1-X10)同表1。

Fig. 7 Impacts of vegetation abrupt change of giant panda habitats in Qionglai Mountains on the results of factor detector (q). Area I represents the area where vegetation abrupt change is detected, and area II represents the area where vegetation abrupt change is not detected. The factors (X1-X10) are the same asTable 1.

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基于稠密Landsat数据的邛崃山大熊猫栖息地植被变化研究

Vegetation change of giant panda habitats in Qionglai Mountains through dense Landsat Data

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