基于LUCC的1986-2022年松花江流域陆地生态系统碳储量动态监测

doi:10.17521/cjpe.2023.0300

摘要/Abstract

摘要： 流域尺度的土地利用/土地覆盖变化(LUCC)动态监测和陆地生态系统碳储量估测, 可以为土地利用优化、提高陆地生态系统碳储量、实现“双碳”目标提供建议。该研究基于1986-2022年Landsat 5 TM和Landsat 8 OLI影像, 应用随机森林获取松花江流域1986-2022年10期高精度的土地利用分布图, 并结合生态系统服务综合价值评估利权衡(InVEST)模型、Mann-Kendall检验和Theil-Sen Median趋势分析, 对松花江流域36年间土地利用类型和生态系统碳储量变化进行动态监测。结果发现, 流域内各土地利用类型面积由大到小依次为耕地>有林地>草地>未利用地>水域>建设用地>疏林地>灌木林地, 耕地、有林地和草地为研究区主要土地利用类型。1986-2022年间耕地面积增加11 462.68 km², 有林地面积减少18 567.21 km²。建设用地为研究区变化最快地类, 变化率为5.3%, 面积增加3 505.82 km²。疏林地变化率为4.7%, 仅次于建设用地, 但由于其面积变化较小, 对流域影响不大。未利用地变化速率为4.5%, 其面积增加了5 385.43 km²。流域内陆地生态系统碳储量空间分布存在明显的空间异质性, 碳储量高值区分布在大小兴安岭和长白山脉; 中值区分布在兴安盟、松嫩平原和三江平原; 低值区分布在大庆和白城。36年间该流域内陆地生态系统碳储量整体呈现减少趋势, 减少区域主要分布在碳储量高值区, 碳储量增加区域则是零星分布。1994、2002和2018年松花江流域生态系统碳储量出现3次恢复, 且均与有林地面积变化有关。在保障已有林地面积不再减少的基础上, 增加有林地面积, 持续开展林业工程, 可以有效阻止碳储量下降, 恢复研究区生态系统碳储量。

关键词: 碳储量, InVEST模型, 土地利用与土地覆盖变化, 分类, 松花江流域

Abstract:

Aims The dynamic monitoring of basin-scale land use and land cover changes and carbon stock estimation of the terrestrial ecosystem can provide suggestions for optimizing land utilization, enhancing terrestrial ecosystem carbon storage, and achieving the “dual carbon” objective.

Methods Based on the Landsat 5 TM and Landsat 8 OLI images from 1986 to 2022, this study employed random forest to obtain ten land use and land cover maps of the Songhua River Basin with high accuracy and conducted dynamic monitoring of land use and land cover change and its ecosystem carbon storage using an integrated valuation of ecosystem services and trade-offs (InVEST) model, Mann-Kendall tests, and Theil-Sen median trend analysis.

Important findings Results showed that farmland has the largest area in the basin, followed by forest land, grasslands, unused lands, water, construction land, sparse forest land, and shrub land. Among them, farmland, forest, and grassland are the dominant land use types in the study area. During the 1986-2022 period, the farmland expanded by 11 462.68 km² while forest land decreased by 18 567.21 km²; the construction land experienced the most significant change rate of 5.3% with an increased area of 3 505.82 km²; the change rate of the sparse forest is 4.7%, ranking second after construction land but having minimal impact on the overall basin due to limited area changes. The change rate of unused land was 4.5%, with an increased area of 5 385.43 km². There was evident spatial heterogeneity in the distribution of the terrestrial ecosystem carbon stocks within the Songhua River Basin, with high carbon stocks predominantly found in Da Hinggan Mountains and Xiao Hinggan Mountains as well as the Changbai Mountains. The median carbon stock values were observed in the Hinggan League, Songnen Plain, and Sanjiang Plain. In contrast, the areas with low carbon values were observed in Daqing and Baicheng. Over the 36 years, there was an overall decline in carbon storage within the basin, primarily concentrated in the regions initially characterized by high carbon stock values. However, the area with increased carbon stock is scattered in the basin. Notably, three recovery instances of ecosystem carbon stock occurred in 1994, 2002, and 2018 within the Songhua River Basin, all related to the changes in forest land. Based on ensuring no reduction of current forest land, it is recommended to expand forest land and continue implementing forestry projects to effectively prevent further depletion of terrestrial ecosystem carbon storage in the Songhua River Basin.

Key words: carbon storage, InVEST model, land use/land cover change, classification, Songhua River Basin

张智洋, 赵颖慧, 甄贞. 基于LUCC的1986-2022年松花江流域陆地生态系统碳储量动态监测. 植物生态学报, 2024, 48(10): 1274-1290. DOI: 10.17521/cjpe.2023.0300

ZHANG Zhi-Yang, ZHAO Ying-Hui, ZHEN Zhen. Dynamic monitoring of carbon storage of the terrestrial ecosystem in Songhua River Basin from 1986 to 2022 based on land use and land cover change. Chinese Journal of Plant Ecology, 2024, 48(10): 1274-1290. DOI: 10.17521/cjpe.2023.0300

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0300

https://www.plant-ecology.com/CN/Y2024/V48/I10/1274

图/表 13

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年份 Year	陆地卫星 Landsat	传感器 Sensor	空间分辨率 Spatial resolution (m)	影像数量 Number of images
1986, 1990, 1994, 1998, 2002, 2006, 2010	Landsat 5	TM	30	6 274
2014, 2018, 2022	Landsat 8	OLI	30	3 132

年份 Year	陆地卫星 Landsat	传感器 Sensor	空间分辨率 Spatial resolution (m)	影像数量 Number of images
1986, 1990, 1994, 1998, 2002, 2006, 2010	Landsat 5	TM	30	6 274
2014, 2018, 2022	Landsat 8	OLI	30	3 132

分类系统 Classification system	面积占比 Proportion of area (%)	样本数量 Number of samples	根据目视解译调整后样本数 Adjusted sample according to visual interpretation
耕地 Farmland	38.9	1 945	2 165
有林地 Forest land	37.5	1 875	1 833
草地 Grassland	9.2	460	375
水域 Water	1.8	90	122
建设用地 Construction land	2.5	125	130
未利用地 Unused land	6.5	325	205
灌木林地 Shrub land	1.7	85	70
疏林地 Sparse forest land	1.9	95	100
总计 Sum	100	5 000	5 000

分类系统 Classification system	面积占比 Proportion of area (%)	样本数量 Number of samples	根据目视解译调整后样本数 Adjusted sample according to visual interpretation
耕地 Farmland	38.9	1 945	2 165
有林地 Forest land	37.5	1 875	1 833
草地 Grassland	9.2	460	375
水域 Water	1.8	90	122
建设用地 Construction land	2.5	125	130
未利用地 Unused land	6.5	325	205
灌木林地 Shrub land	1.7	85	70
疏林地 Sparse forest land	1.9	95	100
总计 Sum	100	5 000	5 000

特征(个数) Feature (number)	Landsat 5 TM	特征(个数) Feature (number)	Landsat 8 OLI
原始波段 Original band (6)	B_i (i = 1, 2, 3, 4, 5, 7)	原始波段 Original band (7)	B_i (i = 1, 2, 3, 4, 5, 6, 7)
波段组合 Band combination (6)	B24, B345, B53, B547, B74, VIS234	波段组合 Band combination (10)	Albedo, B₄/Albedo, B24, B74, B76, B547, B65, B345, B53, VIS234
植被指数 Vegetation index (16)	ARVI, DVI, EVI, LSWI, MSAVI, MSR, MVI5, MVI7, NDVI, NDWI, NLI, PVI, RDVI, RVI, SAVI, SLAVI	植被指数 Vegetation index (18)	NDVI, RDVI, NLI, MVI7, MVI5, SLAVI, MSR, PVI, MSAVI, ARVI, SAVI, ND563, EVI, RVI, DVI, NDWI, NDBI, LSWI
纹理特征 Texture feature (102)	B_i_asm, B_i_contrast, B_i_corr, B_i_dent, B_i_diss, B_i_dvar, B_i_ent, B_i_idm, B_i_imcorr1, B_i_imcorr2, B_i_inertia, B_i_prom, B_i_savg, B_i_sent, B_i_shade, B_i_svar, B_i_var	纹理特征 Texture feature (119)	B_i_asm, B_i_contrast, B_i_corr, B_i_dent, B_i_diss, B_i_dvar, B_i_ent, B_i_idm, B_i_imcorr1, B_i_imcorr2, B_i_inertia, B_i_prom, B_i_savg, B_i_sent, B_i_shade, B_i_svar, B_i_var