非洲地域功能空间演变的时空分异与驱动机制——基于时序遥感数据和可解释机器学习分析
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尹力(1997—),男,陕西宝鸡人,博士生,研究方向为国土空间规划。E-mail: Sliver_Yinli@126.com |
收稿日期: 2024-06-04
修回日期: 2024-09-09
网络出版日期: 2025-03-24
基金资助
国家自然科学基金项目(42471304)
教育部人文社会科学研究规划基金项目(23YJA630003)
中央高校基本科研业务费专项资金(2042024kf0029)
Spatiotemporal differentiation and driving mechanisms of territorial functional space change in Africa: Based on time series remote sensing data and interpretable machine learning
Received date: 2024-06-04
Revised date: 2024-09-09
Online published: 2025-03-24
Supported by
National Natural Science Foundation of China(42471304)
Humanities and Social Sciences Foundation of Ministry of Education of China(23YJA630003)
Fundamental Research Funds for the Central Universities(2042024kf0029)
尹力 , 魏伟 , 李泓锐 , 张轲 , 刘立阳 , 薄立明 . 非洲地域功能空间演变的时空分异与驱动机制——基于时序遥感数据和可解释机器学习分析[J]. 地理科学进展, 2025 , 44(3) : 594 -613 . DOI: 10.18306/dlkxjz.2025.03.012
A scientific understanding of the spatial pattern change and formation mechanisms of Africa's territorial functions is crucial for fundamental research on Africa's land resources. It also aligns with China's Going Global strategy and supports the development of a China-Africa community with a shared future. Using China's territorial dominant functions of urbanization, food security, and ecological security as spatial reference benchmarks, this study classified Africa's territorial space into three major functional areas: urban-rural construction, agricultural production, and ecological protection. By applying spatiotemporal cubes, spatial transformation intensity models, and interpretable machine learning, this research analyzed the spatial evolution characteristics and regional disparities of Africa's territorial functions from 2002 to 2022. Furthermore, the study examined the mechanisms behind these changes by exploring the importance and interaction strengths of key driving factors. The results indicate that: 1) Over the study period, approximately 3.97% of Africa's territorial functional spaces had undergone structural transformation. Urban-rural construction space had expanded significantly by 9.71%, while the per capita scale of construction land had decreased by 33.78%, highlighting an increasing imbalance between population and available land. Agricultural production space had shown a fluctuating decline, with a distinct zone of drastic change forming along the southern edge of the Sahara Desert, while the internal structure of ecological protection space had significantly changed, albeit in an optimizing trend overall. 2) A clear high-dynamic transformation zone had emerged between 5°N and 15°N, where the dynamic mutual transformation between agricultural production and ecological protection spaces was most pronounced. Despite ongoing desert greening efforts, significant land degradation persisted, leading to the expansion-related structural transformations of urban-rural construction space being less prominent on a continental scale. 3) Socioeconomic development and population growth had limited effects on the structural changes of Africa's territorial functional spaces at a continental scale. Instead, variations in hydrological conditions such as atmospheric vapor pressure, temperature, and precipitation, as well as geographical factors like latitude and topographic relief, played a decisive role. Africa's territorial functional space patterns are determined by spatial transformation processes with varying scales, intensities, and driving mechanisms. Future Chinese investments and spatial development strategies in Africa must not only take into account Africa's own unique spatial evolution processes and the underlying patterns that drive these transformations, but also incorporate a thorough understanding of the continent's diverse geographical and socioeconomic contexts. It is crucial to fully leverage the spatial differentiation characteristics, including regional disparities in land use, ecological zones, and development potentials, as well as the emerging trends of its territorial functional spaces. By doing so, these strategies can maximize the comprehensive spatial benefits, fostering sustainable development outcomes that align with both African realities and Chinese strategic objectives in the region, ultimately fostering a mutually beneficial partnership.
表1 非洲地域功能空间分类与国际地圈—生物圈计划(IGBP)中土地利用类型的衔接Tab.1 Matching of Africa's territorial functional space classification and land use types of the International Geosphere-Biosphere Program (IGBP) |
| 非洲地域功能空间 | IGBP土地利用类型及内涵 | 地域主导功能 | |
|---|---|---|---|
| 一级空间 | 二级空间 | ||
| 城乡建设空间 | 城市及建筑用地,包括不透水表面面积至少占30%,以建筑材料、以柏油/沥青为材料的构筑物及车辆与交通设施用地 | 承载城乡发展功能 | |
| 农业生产空间 | 耕地和半混合耕地,包括耕地面积至少占60%的区域,或40%~60%的小规模种植地与天然树木、灌木和草本植被混杂在一起的区域 | 粮食生产与粮食安全 | |
| 生态保护空间 | 绿色生态空间 | 包括覆盖率大于60%且冠高大于2 m的常绿针叶林、常绿阔叶林、落叶针叶林、落叶阔叶林和混交林以及冠高1~2 m的封闭灌木林、开阔灌木林;树木覆盖率30%~60%且冠高大于2 m的热带稀树草原、覆盖率10%~30%的热带草原及草地;至少有60%区域被永久性覆盖的水体、水覆盖率为30%~60%的湿地;每年至少超10个月且60%区域被冰雪覆盖的永久性冰川积雪区 | 提供水源涵养、生物多样性、水土保持等正向生态服务功能 |
| 其他生态空间 | 至少有60%区域是植被覆盖率小于10%的无植被裸地,包括沙地、盐碱地、岩石和裸土地等用地 | 需要生态恢复与修复 | |
注:资料来源于MODIS Collection6.1(C61)土地覆被数据产品用户指南,经翻译与整理。 |
图2 非洲2002—2022年地域功能空间规模变化及城乡建设空间—人口关系分析Fig.2 Scale change of territorial functional space and the spatial-demographic relationship of urban and rural construction in Africa during 2002-2022 |
表2 非洲地域功能空间演变驱动因素的变量说明及其统计特征Tab.2 Variable description and statistical characteristics of the driving factors of Africa's territorial space change |
| 可解释机器学习变量 | 变量统计特征 | |||||||
|---|---|---|---|---|---|---|---|---|
| 类型 | 名称 | 符号 | 驱动因素含义 | 单位 | 均值 | 标准差 | ||
| 自然地理 | 海拔 | elev | 平均海拔高度 | m | 611.57 | 504.57 | ||
| 坡度 | slope | 平均坡度,由高程数据二次计算得出 | (°) | 2.22 | 2.63 | |||
| 地形起伏度 | relief | 单元内海拔最大值与最小值的差值 | m | 908.27 | 835.70 | |||
| 纬度 | lat | 以赤道为界的大地纬度,值域在0~90°之间 | (°) | 13.33 | 10.40 | |||
| 温度 | temp | 首末两年平均气温的差值 | ℃ | 0.07 | 0.39 | |||
| 降水量 | pre | 首末两年平均降水量的差值 | mm | 4.41 | 14.77 | |||
| 大气水汽压 | vap | 首末两年大气中水汽压含量的差值 | hPa | 0.04 | 0.44 | |||
| 潜在蒸散量 | pet | 首末两年区域下垫面可能达到的最大蒸散量差值 | mm | -0.01 | 0.06 | |||
| 交通区位 | 距首都距离 | capdst | 距离该国/区域首都或首府的欧氏距离 | km | 255.12 | 205.62 | ||
| 距市中心距离 | ctrdst | 距离地市中心的欧氏距离 | km | 80.87 | 159.97 | |||
| 距河流距离 | rvrdst | 距离三级以上大河的欧氏距离 | km | 214.66 | 365.46 | |||
| 距湖泊距离 | lakedst | 距离面积超过1 km²湖泊的欧氏距离 | km | 612.09 | 672.45 | |||
| 距主要港口距离 | portdst | 距沿海以贸易和货运为主港口的欧氏距离 | km | 521.71 | 414.31 | |||
| 距铁路距离 | raildst | 距主要铁路线的欧氏距离 | km | 183.22 | 292.10 | |||
| 距公路距离 | roaddst | 距主要公路的欧氏距离 | km | 57.95 | 197.01 | |||
| 社会经济 | 人口规模 | pop | 首末两年地区人口总数的差值 | 1000人 | 60.90 | 546.30 | ||
| 国内生产总值 | gdp | 首末两年地区国内生产总值的差值 | 亿元 | 0.49 | 2.75 | |||
| 人类足迹指数 | hfi | 首末两年人类足迹计算量的差值 | — | 1.00 | 1.14 | |||
| 夜间灯光指数 | nlgt | 首末两年区域灯光总强度的差值 | — | 0.19 | 1.67 | |||
| 城镇化率 | urb | 首末两年区域城镇人口占总人口比值的差值 | % | 8.21 | 63.97 | |||
图7 非洲地域功能空间演变中驱动因素的重要程度、交互强度与SHAP探测结果注:环形图中点的大小及颜色深浅表示因子的重要程度,形状越大、颜色越深,表示该因子越重要;线的粗细及颜色表示首末两端所连因子交互作用的强度,线颜色越深、宽度越粗,表示交互作用越强;瀑布图中箭头的颜色及长度表示该因子对整体SHAP值的贡献方向及大小,起负向影响的箭头向下,反之则朝上;E[f(x)]值为该SHAP模型的基准值。 Fig.7 Importance, interaction intensity, and SHAP detection results of the driving factors in the evolution of Africa's territorial functional space |
图8 典型驱动因素的偏相关依赖图及非洲各区域驱动因素重要性探测结果对比注:图a偏相关依赖图中彩色细线条表示非洲不同空间单元中地域功能空间转型规模与该驱动因素的预测关系,颜色越红表示预测值越高、越蓝则越低,黑色粗线条为非洲整体的平均值;图b中方格内“*”表示该驱动因素重要程度计算值大于非洲整体平均值。 Fig.8 Partial correlation diagram of typical driving factors and comparison of the detection results of the importance of driving factors in various regions of Africa |
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