Enterprise AI Research Analysis
Estimation of Urban Above-Ground Vegetation Carbon Density and Analysis of Topography-Modulated Spectral Responses in Shenzhen, China
Authors: Guangping Qie, Minzi Wang, Guangxing Wang
Journal: Remote Sens. | Year: 2026 | DOI: 10.3390/rs18050807
Executive Impact Summary
Accurate urban carbon density mapping is crucial for climate resilience and sustainable urban planning. This study developed an explainable AI framework using Landsat 8 data and XGBoost to estimate urban above-ground vegetation carbon density (UAGVCD) in Shenzhen, a complex urban environment. The framework utilizes spatial block cross-validation and SHAP analysis to provide robust, interpretable predictions, accounting for spatial autocorrelation and topography's influence on spectral responses. Achieved high accuracy (validation R² = 0.617 ± 0.055 with 5 km blocks), outperforming traditional methods and offering a scalable solution for urban carbon sink assessment. Crucially, it reveals that topography significantly modulates spectral signals, implying that generic models miss vital ecological context, leading to more reliable and ecologically informed urban carbon management strategies.
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Validation R² (5km block)
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Validation RMSE (5km block)
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Field Plots Measured
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Spatial Resolution (Landsat/DEM)
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Field Data Collection & Preprocessing
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Landsat 8 & DEM Feature Extraction
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XGBoost Model Training
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Spatial Block Cross-Validation
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SHAP Interpretation & Interaction Analysis
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Wall-to-Wall Spatial Prediction
Our innovative framework integrates diverse remote sensing data with advanced machine learning, ensuring spatially robust and interpretable carbon density mapping for complex urban ecosystems.
| Block Size | Validation R² (Mean ± SD) | Validation RMSE (Mean ± SD) | Key Implications |
|---|---|---|---|
| 2 km | 0.604 ± 0.109 | 13.66 ± 2.26 Mg ha⁻¹ |
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| 5 km | 0.617 ± 0.055 | 10.25 ± 1.39 Mg ha⁻¹ |
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| 10 km | 0.380 ± 0.297 | 15.41 ± 7.35 Mg ha⁻¹ |
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Evaluating XGBoost model generalization across different spatial block sizes highlights the critical balance between mitigating spatial autocorrelation and preserving local ecological information. The 5 km block emerged as optimal for robust prediction in Shenzhen's heterogeneous urban landscape.
61.7%
Validation R² in Spatially Independent Urban Carbon Prediction
SHAP analysis reveals that elevation acts as a key environmental modulator, reshaping spectral-carbon relationships. Higher elevations strengthen positive spectral contributions, while lower elevations attenuate them, indicating that terrain is not merely an additive factor but a fundamental context for interpreting vegetation carbon dynamics.
Strategic Urban Carbon Management in Shenzhen
Challenge: Rapid urbanization in Shenzhen creates a highly fragmented and heterogeneous landscape, making accurate UAGVCD estimation critical but challenging. Traditional methods struggle with spectral mixing, topographic effects, and spatial autocorrelation.
Solution: Our XGBoost model, validated with 5 km spatial blocks and interpreted via SHAP, effectively disentangles complex spectral-topographic interactions. It generates reliable, pixel-level carbon density maps, identifying key carbon sinks and revealing how terrain modulates vegetation responses.
Outcome: The spatially explicit UAGVCD maps enable data-driven urban planning, guiding strategic investments in green infrastructure and conservation. By understanding topography-driven carbon dynamics, Shenzhen can optimize its carbon sequestration efforts, enhancing urban resilience and contributing to climate mitigation goals.
Shenzhen, a rapidly urbanizing megacity with complex terrain, presents an ideal use case. Our model provides granular UAGVCD maps, essential for identifying high-carbon areas (e.g., mountainous regions, continuous vegetation) and low-carbon areas (e.g., dense urban cores). This supports targeted green infrastructure development, climate-resilient planning, and informed policy-making, ensuring sustainable urban growth and climate mitigation.
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