Department of Environmental Science, Yazd University, Yazd, Iran.
Department of Computer Engineering, Yazd University, Yazd, Iran.
Environ Monit Assess. 2018 Mar 26;190(4):250. doi: 10.1007/s10661-018-6618-2.
Scenario-based land surface temperature (LST) modeling is a powerful tool for adopting proper urban land use planning policies. In this study, using greater Isfahan as a case study, the artificial neural network (ANN) algorithm was utilized to explore the non-linear relationships between urban LST and green cover spatial patterns derived from Landsat 8 OLI imagery. The model was calibrated using two sets of variables: Normalized Difference Built Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Furthermore, Compact Development Scenario (CDS) and Green Development Scenario (GDS) were defined. The results showed that GDS is more successful in mitigating urban LST (mean LST = 40.93) compared to CDS (mean LST = 44.88). In addition, urban LST retrieved from the CDS was more accurate in terms of ANOVA significance (sig = 0.043) than the GDS (sig = 0.010). The findings of this study suggest that developing green spaces is a key strategy to combat against the risk of LST concerns in urban areas.
基于情景的地表温度（LST）建模是采用适当的城市土地利用规划政策的有力工具。本研究以大伊斯法罕为例，利用人工神经网络（ANN）算法来探索城市 LST 与 Landsat 8 OLI 图像得出的绿地空间格局之间的非线性关系。该模型使用两组变量进行校准：归一化差异建筑指数（NDBI）和归一化差异植被指数（NDVI）。此外，还定义了紧凑发展情景（CDS）和绿色发展情景（GDS）。结果表明，与 CDS（平均 LST=44.88）相比，GDS 更成功地缓解了城市 LST（平均 LST=40.93）。此外，从 CDS 中检索到的城市 LST 在 ANOVA 显著性方面更准确（sig=0.043），而 GDS 则不太准确（sig=0.010）。本研究结果表明，发展绿地是应对城市 LST 风险的关键策略。
