基于遥感大数据的“三北”可持续发展评估：指标、数据与方法

doi:10.11707/j.1001-7488.LYKX20250566

林业科学 ›› 2026, Vol. 62 ›› Issue (7): 50-60.doi: 10.11707/j.1001-7488.LYKX20250566

基于遥感大数据的“三北”可持续发展评估：指标、数据与方法

李晓松^1,²(),杨恒聪^1,²,卢琦^3,^4,*(),雷加强⁵,肖春蕾⁶,崔桂鹏^3,⁴

1. 可持续发展大数据国际研究中心　北京 100094
2. 中国科学院空天信息创新研究院　北京 100094
3. 中国林业科学研究院生态保护与修复研究所　北京 100049
4. 三北工程研究院　磴口 015299
5. 中国科学院新疆生态与地理研究所　乌鲁木齐 830011
6. 中国地质环境监测院　北京 100081

收稿日期:2025-09-15 出版日期:2026-07-10 发布日期:2026-07-16
通讯作者: 卢琦 E-mail:lixs@aircas.ac.cn;luqi@caf.ac.cn
基金资助:
国家林业和草原局揭榜挂帅项目（202401）；科学技术部科技基础资源调查专项项目（2022FY202300）；国家自然科学基金项目（4221101459）。

Sustainable Development Assessment of “Three-North” Based on Big Remote Sensing Data: Indicator, Data, and Method

Xiaosong Li^1,²(),Hengcong Yang^1,²,Qi Lu^3,^4,*(),Jiaqiang Lei⁵,Chunlei Xiao⁶,Guipeng Cui^3,⁴

1. International Research Center of Big Data for Sustainable Development Goals　Beijing 100094
2. Aerospace Information Research Institute, Chinese Academy of Sciences　Beijing 100094
3. Institute of Ecological Conservation and Restoration, Chinese Academy of Forestry　Beijing 100049
4. Institute of Great Green Wall　Dengkou 015299
5. Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences　Urumqi 830011
6. China Institute of Geo-Environment Monitoring　Beijing 100081

Received:2025-09-15 Online:2026-07-10 Published:2026-07-16
Contact: Qi Lu E-mail:lixs@aircas.ac.cn;luqi@caf.ac.cn

摘要/Abstract

摘要：

目的: “三北”地区是我国生态建设的主战场，近半个世纪以来实施以“三北”防护林工程为代表的一系列生态工程，在取得巨大成效的同时也面临气候变化加剧、人地关系冲突等新的挑战。2015年，联合国提出面向2030年的可持续发展目标（SDGs）框架，在这一视角下审视“三北”地区的可持续发展具有重要意义。方法: 通过对全球SDG 指标进行本地化重构和遥感化表达，形成面向“三北”的可计算、可比较的可持续发展评估指标体系。采用相对变化率（斜率/均值）结合 Mann-Kendall 显著性检验统一量化指标变化趋势，实现基于时空连续遥感数据产品的指标量化。利用区域“可实现最优表现”法确定目标值，开展面向2030年目标的可持续发展评估。结果: 构建遵循“相关性?可行性?可观测性”原则的本地化SDG指标体系，明确基于遥感大数据的指标量化与综合方法。应用结果表明，“三北”地区整体SDG综合得分85分，呈现“东高西低”的空间格局。在目标层面，SDG 15和SDG 13表现最为突出，平均得分均在87分左右，反映出生态系统保护与修复和气候行动方面取得明显进展；SDG 6得分最低，仅82分，显示水资源管理仍是制约可持续发展的短板。地级与县级行政单元总体得分相近，但更为精细化的县级评估结果凸显区域内部发展不均衡特征。结论: 本研究提出的基于遥感大数据的“三北”可持续发展评估体系，可为评估大规模生态建设地区的可持续发展提供可操作工具，并具备动态更新和跨尺度推广的能力，为科学评估“三北”地区距离实现2030年可持续发展目标提供了一个重要参考。

关键词: “三北”地区, 可持续发展目标, 遥感大数据, 本地化

Abstract:

Objective: The “Three-North” region is the main battlefield of China’s ecological restoration efforts. Over the past half century, a series of large-scale ecological projects, represented by the “Three-North” Shelterbelt Program, have been implemented, and remarkable progress has been achieved while new challenges, such as intensified climate change and growing human–environment conflicts, are also confronted. In 2015, the United Nations proposed the sustainable development goals (SDGs) framework toward 2030, and thus it is highly meaningful to evaluate the sustainable development of the “Three-North” region from this perspective. Method: We localized and reconstructed global SDG indicators using remote-sensing-based approaches to develop a computable and comparable sustainable development assessment system tailored to the “Three-North” region. The relative change rates (slope/mean) combined with the Mann–Kendall significance test were used to to unify the quantification of indicator trends, achieving indicator quantification based on spatiotemporal continuous remote sensing data products. The region’s “best achievable performance” method was used to determine the target values and conduct a sustainable development assessment towards the 2030 goal. Result: A localized SDG indicator system was constructed grounded in relevance, feasibility, and observability. A unified method for indicator quantification and integration was established using big earth data. Results showed that the “Three-North” region had an overall composite score of 85, displaying a spatial pattern of “higher in the east and lower in the west”. At the goal level, SDG 15 and SDG 13 performed best, with an average score of around 87 each, reflecting significant progress in ecosystem conservation, restoration, and climate action. SDG 6 had the lowest score, at only 82, indicating that water resource management remained a major bottleneck for sustainable development. Scores at the prefectural and county levels were generally similar, though the finer county-level results highlighted pronounced intra-regional disparities. Conclusion: Overall, the remote-sensing-based sustainable development assessment system developed in this study provides an operational tool for evaluating the sustainability progress of large-scale ecological restoration areas. With capabilities for dynamic updates and cross-scale application, it offers an important reference for systematically tracking how far the “Three-North” region is from achieving the 2030 sustainable development goals.

Key words: “Three-North” region, sustainable development goals, big remote sensing data, SDG localization

中图分类号:

S732
P237

李晓松,杨恒聪,卢琦,雷加强,肖春蕾,崔桂鹏. 基于遥感大数据的“三北”可持续发展评估：指标、数据与方法[J]. 林业科学, 2026, 62(7): 50-60.

Xiaosong Li,Hengcong Yang,Qi Lu,Jiaqiang Lei,Chunlei Xiao,Guipeng Cui. Sustainable Development Assessment of “Three-North” Based on Big Remote Sensing Data: Indicator, Data, and Method[J]. Scientia Silvae Sinicae, 2026, 62(7): 50-60.

图/表 5

表1

“三北”可持续发展评估指标体系"

目标 Goal	指标名称 Indicator name	原指标编号/ 改编情况 Original indicator number/ adaptation status	数据来源 Data source	指标解释 Indicator explanation
SDG 15	森林面积占比 Forest area proportion	15.1.1	遥感分类数据产品 Earth observation classification products	基于遥感提取的森林面积占陆地总面积的比例，用于表征区域森林资源状况 The proportion of forest area to total land area derived from remote sensing, used to characterize the status of regional forest resources
	湿地面积占比 Wetland area proportion	15.1（改编 Adaptation）	遥感分类数据产品 Earth observation classification products	基于遥感提取的湿地面积占陆地总面积的比例，用于表征区域湿地资源状况 The proportion of wetland area to total land area derived from remote sensing, used to characterize the status of regional wetland resources
	土地净恢复比例 Net land restoration ratio	15.3.1（改编 Adaptation）	多源遥感评估（土地覆盖、土地生产力） Multi-source remote sensing assessment （land cover, land productivity）	观测期内恢复土地面积与退化土地面积差值占总土地面积的比例 The proportion of the difference between restored and degraded land areas relative to the total land area over the observation period
	重要生物多样性场所被保护比例 Percentage of key biodiversity areas protected	15.1.2	保护地与重要生物多样性场所边界数据 Boundary data for protected areas and key biodiversity areas	量化重要生物多样性场所受保护情况 Quantifying the proportion of key biodiversity areas covered by protected areas
SDG 6	河流沉积物浓度 Sediment concentration in river	6.3.2（改编 Adaptation）	河流沉积物浓度产品 Sediment concentration product	反映生态修复相关水土流失情况 Reflecting soil erosion status related with ecological restoration
	生态系统水分亏缺 Ecosystem water deficit	6.4.1（改编 Adaptation）	降水量与蒸散发产品 Precipitation and evapotranspiration products	反映生态系统水分限制程度及生态恢复过程中水资源约束状况 Reflecting the degree of water limitation in ecosystems and the constraints imposed by water availability during ecological restoration
	水体面积 Water area	6.6.1	水体分布数据产品 Water body distribution products	反映区域地表水体水量情况 Reflecting the status of surface water availability in the region
SDG 2	单位面积粮食产量 Grain yield per unit area	2.4.1（改编 Adaptation）	作物产量数据产品 Crop yield product	反映粮食产量情况 Reflecting agricultural production conditions
SDG 2	单位面积草地净初级生产力 Net primary productivity per unit area of grassland	2.4.1（改编 Adaptation）	净初级生产力数据产品 Net primary productivity product	反映草地产草量情况 Reflecting grassland productivity
SDG 13	净生态系统生产力 Net ecosystem productivity	13.2.2（改编 Adaptation）	NEP数据产品 NEP product	反映生态系统固碳能力 Reflecting the ecosystem carbon fixing capacity
SDG 13	PM₁₀年均浓度 Annual average concentration of PM₁₀	13.1.1（改编 Adaptation）	PM₁₀遥感反演产品 PM₁₀ product	反映生态修复与沙尘天气与空气质量关系 Reflecting the relationship between ecological restoration and dust events and air quality
SDG 8	人均GDP GDP per capita	8.1.1（改编 Adaptation）	统计年鉴 Statistical yearbook	反映经济发展整体水平、生态环境压力与治理投入能力 Reflecting the overall level of economic development, environmental pressure， and governance investment capacity

表1

图1

表S1

“三北”可持续发展评估本地指标量化①"

序号 Number	指标名称 Indicator name	数据源 Data source	空间分辨率 Spatial resolution	时间范围 Time range	变量构成 Variable composition
1	森林面积占比 Forest area proportion	中国年度土地覆盖CLCD （Yang et al.， 2021）	30 m	2015—2022	年度森林面积 Annual forest area
2	湿地面积占比 Wetland area proportion	中国年度土地覆盖CLCD （Yang et al.，2021）	30 m	2015—2022	年度湿地面积 Annual wetland area
3	土地净恢复比例 Net land restoration ratio	全球土地退化数据集LDN Dataset （李晓松等，2021）	1 km	2015—2022	恢复面积、退化面积 Restored area, degraded area
4	重要生物多样性场所受保护比例 Percentage of important biodiversity sites protected	KBA + China’s Protected Area（BirdLife International, 2025；Fan et al., 2023）	矢量 Vector	－	KBA、保护地 KBA, protected area
5	河流沉积物浓度 Sediment concentration in river	全球河流沉积物浓度数据集 Global river sediment concentration dataset （Sun et al.，2025）	河段级 River section	2015—2020	年均悬浮泥沙浓度 Annual average suspended sediment concentration
6	生态系统水分亏缺 Ecosystem water deficit	CHIRPS precipitation + MODIS ET（Funk et al.，2015；Running et al.，2019）	1 km	2015—2022	年度降水、蒸散发 Annual precipitation and evapotranspiration
7	水体面积 Water area	JRC 全球地表水 JRC global surface water （Pekel et al.，2016）	30 m	2015—2021	年度水体面积 Annual water area
8	单位面积粮食产量 Grain yield per unit area	全球粮食产量数据集（GFSAD/GFEP）（覃星力等，2024） Global food supply and production dataset （GFSAD/GFEP）	5 km	2015—2022	小麦、玉米、水稻、大豆产量 Wheat, corn, rice, and soybean production
9	单位面积草地净初级生产力 Net primary productivity per unit area of grassland	MODIS NPP （Running et al.，2021）	500 m	2015—2022	年度 NPP 值 Annual NPP value
10	净生态系统生产力 Net ecosystem productivity	中国 NEP 数据集（史海静等，2025） China NEP dataset	1 km	2015—2021	NPP、Rh
11	PM₁₀年均浓度 Annual average concentration of PM₁₀	中国 PM₁₀ 高分辨率数据集（韦晶等，2023） China PM₁₀ high-resolution dataset	1 km	2015—2022	年均 PM₁₀ Annual average PM10
12	人均 GDP GDP per capita	统计年鉴 Statistical yearbook	市级/县级 Municipal/ county-level	2022	人均 GDP Per capita GDP

表S1

图2

图3

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基于遥感大数据的“三北”可持续发展评估：指标、数据与方法

Sustainable Development Assessment of “Three-North” Based on Big Remote Sensing Data: Indicator, Data, and Method

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