Environmental interpretation of spatial heterogeneity in the trade-offs and synergies of land use functions: A study based on the XGBoost-SHAP model

doi:10.1007/s40333-025-0058-y

Journal of Arid Land

2025, Vol. 17

Issue (10): 1378-1401 DOI: 10.1007/s40333-025-0058-y CSTR: 32276.14.JAL.0250058y

Research article

Environmental interpretation of spatial heterogeneity in the trade-offs and synergies of land use functions: A study based on the XGBoost-SHAP model

FENG Haoyuan¹^,², ZHANG Xuebin¹^,^*(

), SHI Peiji¹, SHI Jing³, WANG Ziyang¹

¹College of Geography and Environmental Sciences, Northwest Normal University, Lanzhou 730070, China
²Engineering Research Center for Ecological and Environmental Damage Assessment of Gansu Province, Northwest Normal University, Lanzhou 730070, China
³College of Ecology, Lanzhou University, Lanzhou 730000, China

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Abstract

Accurately revealing the spatial heterogeneity in the trade-offs and synergies of land use functions (LUFs) and their driving factors is imperative for advancing sustainable land utilization and optimizing land use planning. This is especially critical for ecologically vulnerable inland river basins in arid regions. However, existing methods struggle to effectively capture complex nonlinear interactions among environmental factors and their multifaceted relationships with trade-offs and synergies of LUFs, especially for the inland river basins in arid regions. Consequently, this study focused on the middle reaches of the Heihe River Basin (MHRB), an arid inland river basin in northwestern China. Using land use, socioeconomic, meteorological, and hydrological data from 2000 to 2020, we analyzed the spatiotemporal patterns of LUFs and their trade-off and synergy relationships from the perspective of production, living, ecological functions. Additionally, we employed an integrated Extreme Gradient Boosting (XGBoost)-SHapley Additive exPlanations (SHAP) framework to investigate the environmental factors influencing the spatial heterogeneity in the trade-offs and synergies of LUFs. Our findings reveal that from 2000 to 2020, the production, living, and ecological functions of land use within the MHRB exhibited an increasing trend, demonstrating a distinct spatial pattern of ''high in the southwest and low in the northeast''. Significant spatial heterogeneity defined the trade-off and synergistic relationships, with trade-offs dominating human activity-intensive oasis areas, while synergies prevailed in other areas. During the study period, synergistic relationships between production and living functions and between production and ecological functions were relatively robust, whereas synergies in living-ecological functions remained weaker. Natural factors (digital elevation model (DEM), annual mean temperature, Normalized Difference Vegetation Index (NDVI), and annual precipitation) emerged as the primary factors driving the trade-offs and synergies of LUFs, followed by socioeconomic factors (population density, Gross Domestic Product (GDP), and land use intensity), while distance factors (distance to water bodies, distance to residential areas, and distance to roads) exerted minimal influence. Notably, the interactions among NDVI, annual mean temperature, DEM, and land use intensity exerted the most substantial impacts on the relationships among LUFs. This study provides novel perspectives and methodologies for unraveling the mechanisms underlying the spatial heterogeneity in the trade-offs and synergies of LUFs, offering scientific insights to inform regional land use planning and sustainable natural resource management in inland river basins in arid regions.

Key words： production function living function ecological function trade-offs and synergies Extreme Gradient Boosting (XGBoost) SHapley Additive exPlanations (SHAP) Heihe River Basin

Received: 25 April 2025 Published: 31 October 2025

Corresponding Authors: *ZHANG Xuebin (Email: zhangxb@nwnu.edu.cn)

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Cite this article:

FENG Haoyuan, ZHANG Xuebin, SHI Peiji, SHI Jing, WANG Ziyang. Environmental interpretation of spatial heterogeneity in the trade-offs and synergies of land use functions: A study based on the XGBoost-SHAP model. Journal of Arid Land, 2025, 17(10): 1378-1401.

URL:

http://jal.xjegi.com/10.1007/s40333-025-0058-y OR http://jal.xjegi.com/Y2025/V17/I10/1378

Fig. 1 Overview of the study area (middle reaches of the Heihe River Basin) based on digital elevation model (DEM; a) and spatial distribution of land use types in the study area (b)

Table 1 Description and sources of data used in this study

First level	Second level	Equation	Variable interpretation
Production function	Food production	$C{{P}_{i}}=C{{P}_{\text{sum}}}\times \frac{\text{NDV}{{\text{I}}_{i}}}{\text{NDV}{{\text{I}}_{\text{sum}}}}$	CP_i and CP_sum are the food production allocated to the grid cell i and the study area (t/km²), respectively; and NDVI_i and NDVI_sumare the NDVI values of grid cell i and the study area, respectively.
Living function	Residential carrying capacity	$V{{P}_{i}}=POP{{D}_{i}}\times RAR{{E}_{i}}\times CHS$	VP_i is the residential carrying capacity value of grid cell i (CNY); POPD_i is the population density of grid cell i (persons/km²); RARE_i is the per capita residential area in grid cell i for the current year (m²/person); and CHS is the commercial housing sales (CNY).
Living function	Economic carrying capacity	${{G}_{i}}=\sum{\frac{{{G}_{s}}}{{{S}_{s}}}}\times {{S}_{i}}\times \left( 1+{{I}_{i}} \right)$	G_i and G_s are the secondary and tertiary industries' output values of grid cell i and the study area (CNY), respectively; S_i and S_s are the sum of urban-rural construction land area and nighttime light area of grid cell i and the study area (hm²), respectively; and I_iis the average nighttime light intensity in grid cell i.
Ecological Function	Carbon storage	${{C}_{j}}={{C}_{j\text{-abov}e}}+{{C}_{j\text{-below}}}+{{C}_{j\text{-soil}}}+{{C}_{j\text{-dead}}}$ ${{C}_{\text{Total}}}=\sum\limits_{j=1}^{n}{{{C}_{j}}\times {{A}_{j}}}$	C_j, C_j-_above, C_j-_below, C_j-_soil, and C_j-_dead are the carbon density (kg C/m²), aboveground vegetation carbon density (kg C/m²), belowground vegetation carbon density (kg C/m²), soil carbon density (kg C/m²), and carbon density of dead organic matter (kg C/m²) of land use type j, respectively; C_Totalis the total ecosystem carbon storage (t C); A_j is the area of land use type j (hm²); and n is the number of land use types.
	Windbreak and sand fixation	$S{{L}_{sv}}=S{{L}_{s}}-S{{L}_{v}}$ $SL=\frac{{{Q}_{y}}}{y}$ ${{Q}_{\text{max}}}=109.8\left( WF\times EF\times SCF\times {K}'\times COG \right)$ $C=150.71{{(WF\times EF\times SCF\times COG)}^{-0.3711}}$	SL_sv, SL_s, SL_v, and SL are the potential windbreak and sand fixation capacity (kg/m²), potential soil wind erosion (kg/m²), actual soil wind erosion (kg/m²), and soil wind erosion modulus (kg/m²), respectively; y is the length of the land parcel (m); Q_y is the sand flux at land parcel length y (kg/m); Q_max is the maximum sand transport capacity of wind (kg/m); C is the critical land parcel length (m); WF is the meteorological factor (kg/m); EF is the soil erodibility component; SCF is the soil crust factor; K′ is the soil roughness factor; and COG is the vegetation factor, including flat-laying vegetation, upright crop residues, and vegetation canopy.
	Water yield	$W{{Y}_{ij}}=\left( 1-\frac{AE{{T}_{ij}}}{Pr{{e}_{i}}} \right)\times Pr{{e}_{i}}$	WY_ij is the water yield of land use type j in grid cell i (m³); AET_ij is the annual actual evapotranspiration of land use type j in grid cell i (mm); and Pre_i is the annual precipitation in grid cell i (mm).
	Habitat quality	${{Q}_{ij}}={{H}_{ij}}\times \left[ 1-\left( \frac{D_{ij}^{z}}{D_{ij}^{z}+{{K}^{Z}}} \right) \right]$	Q_ij is the habitat quality of land use type j in grid cell i; H_ij is the habitat suitability of land use type j in grid cell i; D_ij is the disturbance level of land use type j in grid cell i; z is the default model parameter, set to 2.5; and K is the half-saturation parameter, typically set to half of the maximum habitat degradation value.

Table 2 Calculation of production, living, and ecological sub-functions

Fig. 2 Spatial distribution of 12 influencing factors on the trade-offs and synergies of land use functions (LUFs). (a), X 1 (population density); (b), X 2 (Gross Domestic Product, GDP); (c), X 3 (land use intensity); (d), X 4 (distance to residential areas); (e), X 5 (distance to water bodies); (f), X 6 (distance to roads); (g), X 7 (Normalized Difference Vegetation Index, NDVI); (h), X 8 (annual precipitation); (i), X 9 (annual mean temperature); (j), X 10 (slope direction); (k), X 11 (slope); (l), X 12 (DEM).

Fig. 3 Spatial distribution of areas with different levels of production, living, and ecological functions in 2000 (a-c), 2010 (d-f), and 2020 (g-i), as well as area changes in production, living, and ecological functions from 2000 to 2020 (j-l)

Fig. 4 Correlation relationships of different LUFs (production, living, and ecological functions) during 2000-2010 (a1-a3), 2010-2020 (b1-b3), and 2000-2020 (c1-c3). r, correlation coefficient;^***, significance at P<0.001 level. The red line is the linear fitting line for the correlation between the two LUFs.

Fig. 5 Spatial distribution of trade-offs and synergies between different LUFs (production, living, and ecological functions) during 2000-2010 (a-c), 2010-2020 (d-f), and 2000-2020 (g-i)

Table 3 Statistic metrics of RF, SVR, LR, and XGBoost models in fitting trade-offs and synergies between different LUFs (production, living, and ecological functions)

Fig. 6 Comparison between actual and predicted values in fitting trade-offs and synergies in production-living functions using different machine learning models. (a), RF (Random Forest); (b), SVR (Support Vector Regression); (c), LR (Linear Regression); (d), XGBoost (Extreme Gradient Boosting).

Fig. 7 SHapley Additive exPlanations (SHAP) characteristics of environmental factors influencing trade-offs and synergies in production-living functions. (a1), importance of global feature; (a2), importance of local feature; (b1), X 1 (population density); (b2), X 2 (GDP); (b3), X 3 (land use intensity); (b4), X 4 (distance to residential areas); (b5), X 5 (distance to water bodies); (b6), X 6 (distance to roads); (b7), X 7 (NDVI); (b8), X 8 (annual precipitation); (b9), X 9 (annual mean temperature); (b10), X 10 (slope direction); (b11), X 11 (slope); (b12), X 12 (DEM).

Fig. 8 SHAP characteristics of environmental factors influencing trade-offs and synergies in production- ecological functions. (a1), importance of global feature; (a2), importance of local feature; (b1), X 1 (population density); (b2), X 2 (GDP); (b3), X 3 (land use intensity); (b4), X 4 (distance to residential areas); (b5), X 5 (distance to water bodies); (b6), X 6 (distance to roads); (b7), X 7 (NDVI); (b8), X 8 (annual precipitation); (b9), X 9 (annual mean temperature); (b10), X 10 (slope direction); (b11), X 11 (slope); (b12), X 12 (DEM).

Fig. 9 SHAP characteristics of environmental factors influencing trade-offs and synergies in living-ecological functions. (a1), importance of global feature; (a2), importance of local feature; (b1), X 1 (population density); (b2), X 2 (GDP); (b3), X 3 (land use intensity); (b4), X 4 (distance to residential areas); (b5), X 5 (distance to water bodies); (b6), X 6 (distance to roads); (b7), X 7 (NDVI); (b8), X 8 (annual precipitation); (b9), X 9 (annual mean temperature); (b10), X 10 (slope direction); (b11), X 11 (slope); (b12), X 12 (DEM).

Fig. 10 Cross-dependence of SHAP values for key environmental factors influencing trade-offs and synergies between different LUFs. (a1-a3), production-living functions; (b1-b3), production-ecological functions; (c1-c3), living-ecological functions.


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