A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN
A New Method for Quantitative Analysis of Driving Factors for Vegetation Coverage Change in Mining Areas: GWDF-ANN
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矿区植被覆盖变化驱动因素定量分析新方法：GWDF-ANN
DOI：
10.3390/rs14071579
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发表时间：
2022-04-01
期刊：
影响因子：
5
通讯作者：
Zhang, Yicong
中科院分区：
文献类型：
--
作者：
Li, Jun;Qin, Tingting;Zhang, Yicong
采矿对植被覆盖造成了相当大的破坏，特别是草原。探讨各种因素对植被覆盖变化的具体贡献具有重要意义。在本研究中，植被覆盖度分数（FVC）被用作植被覆盖度的代理指标。我们构建了50套胜利煤田FVC及其驱动因素的地理加权人工神经网络模型。基于微分思想，提出了地理加权微分因子人工神经网络（GWDF-ANN）来量化不同驱动因素对矿区FVC变化的贡献。研究要点如下：（1）对于50个模型，平均RMSE为0.052。 RMSE 最低为 0.007，最高为 0.112。对于MRE，平均值为0.007，最低为0.001，最高为0.023。 GWDF-ANN 模型适用于量化矿区 FVC 变化。 (2)降水量和气温是FVC变化的主要驱动因素。降水贡献32.45%，气温贡献24.80%，采矿贡献22.44%，城市扩张贡献14.44%，地形贡献5.87%。 (3)随着时间的推移，降水和气温的贡献呈现下降趋势，而采矿和城市扩张则呈现正向轨迹。对于地形而言，其贡献总体上保持不变。 （4）随着距矿区距离的增加，采矿贡献逐渐减小。 200 m 处，采矿贡献率为 26.69%；在2000米处，该值下降至17.8%。 (5) 采矿对植被覆盖度具有年际和空间上的累积效应。本研究为认识矿区植被覆盖变化机制提供重要支撑。
Mining has caused considerable damage to vegetation coverage, especially in grasslands. It is of great significance to investigate the specific contributions of various factors to vegetation cover change. In this study, fractional vegetation coverage (FVC) is used as a proxy indicator for vegetation coverage. We constructed 50 sets of geographically weighted artificial neural network models for FVC and its driving factors in the Shengli Coalfield. Based on the idea of differentiation, we proposed the geographically weighted differential factors-artificial neural network (GWDF-ANN) to quantify the contributions of different driving factors on FVC changes in mining areas. The highlights of the study are as follows: (1) For the 50 models, the average RMSE was 0.052. The lowest RMSE was 0.007, and the highest was 0.112. For the MRE, the average value was 0.007, the lowest was 0.001, and the highest was 0.023. The GWDF-ANN model is suitable for quantifying FVC changes in mining areas. (2) Precipitation and temperature were the main driving factors for FVC change. The contributions were 32.45% for precipitation, 24.80% for temperature, 22.44% for mining, 14.44% for urban expansion, and 5.87% for topography. (3) Over time, the contributions of precipitation and temperature exhibited downward trends, while mining and urban expansion showed positive trajectories. For topography, its contribution remains generally unchanged. (4) As the distance from the mining area increases, the contribution of mining gradually decreases. At 200 m away, the contribution of mining was 26.69%; at 2000 m away, the value drops to 17.8%. (5) Mining has a cumulative effect on vegetation coverage both interannually and spatially. This study provides important support for understanding the mechanism of vegetation coverage change in mining areas.